The scale of the disparity is difficult to overstate. Adolescents and young adults, defined in this context as patients between the ages of 15 and 39, and older adults aged 60 and above remain markedly underrepresented in clinical trials despite decades of evidence that trial participation correlates with access to cutting-edge therapies and improved outcomes. The gap is even wider for patients from racially and ethnically minoritized communities, those from lower-income backgrounds, and those treated in community-based settings rather than large academic centers. For a disease category that includes more than 100 distinct blood cancer types, the consequences of this exclusion ripple across the entire research enterprise, limiting the generalizability of trial results and delaying the delivery of new treatments to the very populations that stand to benefit most.

The structural reasons behind these gaps are well documented but notoriously difficult to fix. Eligible patients are often identified late in the course of their care, after treatment decisions have already been made and enrollment windows have closed. Referral pathways between community oncology practices and academic research centers are frequently fragmented, forcing patients and providers to navigate a maze of administrative hurdles at precisely the moment when time is most scarce. Awareness of available trials remains low among both patients and frontline clinicians, and access to dedicated patient navigation services is uneven at best. These barriers compound one another, creating a system in which the patients most likely to benefit from novel therapies are the least likely to encounter them.

Blood cancers add a unique layer of urgency to this problem. Unlike many solid tumors, where patients may have weeks or months to weigh treatment options, blood malignancies such as acute leukemias often demand immediate therapeutic intervention. The clinical tempo leaves little room for the deliberate consideration of clinical trial opportunities, and patients who might otherwise enroll find themselves swept into standard treatment pathways before research options can even be discussed. Any intervention designed to improve trial access in this space must therefore operate faster than the disease itself, identifying eligible patients and surfacing trial options at the very beginning of the care journey rather than after treatment plans have crystallized.

The Columbia-led study will evaluate a coordinated, patient-centered intervention that combines three complementary components. The first is AI-assisted patient identification, using computational tools to scan clinical data and flag patients who meet eligibility criteria for active trials, a task that traditionally consumes enormous amounts of clinician time and is prone to human oversight. The second is streamlined provider support, designed to reduce the administrative burden on oncologists and nurses who wish to refer patients to research programs but lack the time or infrastructure to do so efficiently. The third is personalized patient navigation, pairing patients with trained navigators who can guide them through the logistical, financial, and informational barriers that often stand between eligibility and enrollment.

Together, these components are designed to help patients and healthcare teams identify clinical trial opportunities earlier in the treatment trajectory and to connect eligible individuals with research programs at both academic and community cancer centers. The intervention will be evaluated through Blood Cancer United’s Clinical Trial Support Center, a centralized public resource that will serve as the operational backbone of the study. By embedding the research within an existing national infrastructure, the investigators hope to generate findings that are immediately scalable rather than confined to a single institution or health system.

Eric Cooks, Ph.D., Senior Director of Equity in Access Programs at Blood Cancer United, framed the investment as a necessary step toward systemic change. According to Cooks, advancing equity in cancer care requires evidence-based solutions that can be implemented across healthcare settings to address disparities, and the Columbia study will test innovative approaches to connect more patients with potentially life-saving clinical trials while ensuring that advances in blood cancer research reach all communities, including those historically underrepresented in clinical research. The emphasis on cross-setting implementation reflects a growing recognition in the health services research community that interventions confined to elite academic centers, however effective, cannot close population-level gaps on their own.

The study’s focus on health services research, rather than basic or translational science, is itself significant. Health services research examines how care is organized, delivered, and accessed, asking not whether a treatment works but whether the systems surrounding it allow patients to benefit from it in the first place. In oncology, this lens has become increasingly central as the pace of therapeutic innovation outstrips the capacity of existing care infrastructures to deliver it equitably. A breakthrough therapy that never reaches the patients who need it most is, from a public health perspective, only half a breakthrough, and the Equity in Access Research Program was designed specifically to fund the kind of systems-level evidence needed to close that second half of the gap.

Since its launch in 2021, the program has awarded nearly $21 million in funding to support 16 high-impact health services research studies, making it one of the most substantial philanthropic investments in cancer care delivery science. Blood Cancer United has acknowledged support from AstraZeneca and Syndax for the program and related initiatives focused on reducing healthcare disparities in blood cancer care and treatment. The organization, which has evolved continuously since its founding in 1949 to serve people affected by all blood cancer types, funds research, offers free resources and personalized support, and advocates at state and national levels for more accessible and affordable healthcare.

The implications of the Columbia study extend well beyond blood cancer. If the combined model of AI-assisted identification, provider support, and patient navigation proves effective, the findings could inform policy and practice across the broader oncology landscape, offering a template for how health systems, professional societies, and policymakers approach trial access for other underserved populations. The ultimate goal, as the investigators and funders describe it, is to increase access to clinical trials, improve overall participation, and ensure that every patient, regardless of age, geography, income, or background, has the chance to benefit from the remarkable pace of cancer research advances. In a field where the next breakthrough is always in development, the question of who gets to receive it may prove as consequential as the science itself.

Subject of Research: Health services research on equitable clinical trial access for blood cancer patients

Article Title: Blood Cancer United invests $2.5 million in health services research to improve clinical trial participation

Article References: Blood Cancer United invests $2.5 million in health services research to improve clinical trial participation. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: Blood Cancer United, clinical trials, health equity, blood cancer, patient navigation, Columbia University, adolescents and young adults, older adults, health services research, Equity in Access Research Program, cancer disparities, AI-assisted screening

The study, published in Supportive Care in Cancer, evaluated OpenSYMPRO, a web application built on an existing Dutch symptom monitoring tool called SYMPRO. The original SYMPRO app required enrollment by a healthcare professional, which meant patients could only use it if their hospital participated. OpenSYMPRO removed that gatekeeping entirely: anyone starting systemic therapy such as chemotherapy, immunotherapy, hormonal therapy, or targeted treatment could self-register, complete a short intake about their diagnosis, and begin receiving weekly questionnaires based on the validated Dutch version of the Patient-Reported Outcomes Common Terminology Criteria for Adverse Events, or PRO-CTCAE. Each questionnaire took roughly five minutes and asked about symptom severity, frequency, and interference with daily life over the past week.

The technical architecture was thoughtfully designed. Computerized adaptive testing skipped follow-up questions when patients reported no symptoms, minimizing burden. Predefined alerting algorithms, based on healthcare risk thresholds and specific symptom patterns, prompted users to contact their care team when responses suggested something concerning. Symptom reports were generated across three tabs showing alert-triggering symptoms, overall scores per symptom, and item-level details, which patients were encouraged to share during consultations. Because OpenSYMPRO was deliberately not linked to hospitals, alerts were not forwarded to clinicians directly; instead, the app followed a reactive model in which patients themselves were advised to seek care when alerts appeared. Data were stored securely within the PROFILES registry, and the platform was free, accessible on all mobile devices, and supported by tutorials developed with a patient organization and a dedicated helpdesk.

None of this was enough to generate meaningful uptake. After eighteen months of open availability and six months of hospital-based promotion of the regular SYMPRO tool, just twenty participants had enrolled by logging in at least once. Eighteen came through OpenSYMPRO and two through the hospital route, and neither of the hospital-enrolled patients completed a single questionnaire. Of the full sample, seven were frequent users who completed more than two symptom questionnaires, nine completed only one or two, and four never filled out any at all. The original research plan, which aimed to compare adolescents and young adults using the open platform with older patients using the hospital-based version, had to be abandoned entirely because recruitment fell so far short of expectations.

The research team pivoted to an exploratory question: why had engagement been so low? All twenty participants were invited to give feedback through interviews or questionnaires, and nine responded, including seven within the adolescent and young adult age range and two outside it. Thematic analysis of their accounts surfaced five recurring barriers, and notably, none of them were technical. Participants generally found the platform easy to operate, and no substantial technical problems were reported. One respondent even remarked that the tech part worked well enough. The obstacles were organizational, communicative, and deeply personal.

The first barrier was study overload. Several participants described feeling inundated by invitations to join multiple research projects and digital tools during their cancer trajectory, leaving them confused about each initiative’s purpose. One participant said she was called by so many different people that she honestly did not remember who was from which study anymore. Closely related was perceived overlap: some felt that OpenSYMPRO’s questions closely resembled those from other studies, creating a sense of answering the same things repeatedly. A third theme was simply forgetting. Several participants had completely lost track of the app or no longer remembered its purpose, suggesting that digital tools fade from attention quickly when they are not anchored to routine care. One respondent, contacted for an interview, admitted having no idea what the app was about or why the researchers were calling.

The remaining barriers cut to the heart of what cancer treatment actually feels like. Many participants described the treatment period as intense and emotionally overwhelming, leaving little time or cognitive capacity for even minor additional tasks such as a five-minute questionnaire. As one put it, a lot was happening at the time and she just could not deal with anything extra. Finally, some questioned the app’s added value altogether. One noted that chemotherapy-related symptoms were too severe to forget, making a tracking tool redundant. Another said that if she did not experience a particular side effect, she did not want to be made aware that it could occur, a striking reminder that symptom education can itself become a source of anxiety. Several believed they could communicate their symptoms effectively to clinicians without any app.

These findings matter because the evidence base for digital symptom monitoring is otherwise so strong. Large randomized trials have shown that routine electronic patient-reported outcome monitoring improves health-related quality of life, reduces emergency visits, and in some analyses improves overall survival, likely because alerting algorithms enable intervention before symptoms escalate. But those successes almost always occurred within tightly integrated clinical workflows, where clinicians actively reviewed patient reports and responded to alerts. OpenSYMPRO’s open, patient-initiated model deliberately severed that connection to maximize access, particularly for young adults whose care in the Netherlands is scattered across multiple hospitals and subspecialties. The results suggest that access alone, without visible ties to the care team and clear communication of personal benefit, may be insufficient to sustain engagement, although the authors caution that their exploratory data cannot establish this causally.

The study has important limitations that shape how its lessons should be applied. The sample was very small and clinically heterogeneous, qualitative feedback came from only nine respondents, and data saturation cannot be assumed. Crucially, the researchers could only gather feedback from people who had registered; patients who encountered the tool but chose not to sign up could not be approached, meaning the data illuminate discontinuation far better than initial non-uptake. Age at diagnosis was available for nineteen of twenty participants, but age at study participation was not, complicating interpretation of life-stage effects. The authors also acknowledge that design-related barriers, such as the choice of a web application rather than a downloadable app, visibility, navigation, and the number of steps required to engage, were not systematically evaluated and cannot be excluded, particularly among those who disengaged earliest.

Even so, the recommendations that emerge are concrete and likely to resonate with anyone implementing digital health tools. The authors argue that research teams and clinicians should coordinate introductions to avoid duplicating requests, ideally harmonizing data collection within a single integrated app. Digital symptom monitoring should be woven into routine care rather than offered as a standalone extra, with healthcare professionals actively involved, as demonstrated by successful European initiatives used by thousands of patients. Passive email reminders should be replaced or supplemented by personalized strategies tied to treatment milestones and concrete clinical decisions, ideally delivered through familiar hospital systems. Flexible participation models that allow patients to pause during intensive treatment phases could respect the realities of their capacity. Whether adolescents and young adults need additional design adaptations tailored to their developmental stage remains an open question that future co-design studies will need to answer. What this study makes clear is that the hardest problems in digital oncology are not computational but human: attention, trust, timing, and the simple question every patient implicitly asks of a new tool, which is what exactly is in it for me.

Subject of Research: Real-world uptake and engagement with a digital symptom-monitoring tool among patients with cancer receiving systemic therapy

Article Title: Digital symptom monitoring in cancer care: an exploratory evaluation of low uptake and engagement

Article References: Mols, F., Hendriks, M., Bol, N., Sleeman, S., Husson, O., van Eenbergen, M., & van den Hurk, C. (2026). Digital symptom monitoring in cancer care: an exploratory evaluation of low uptake and engagement. Supportive Care in Cancer, 34(10), Article 1045. https://doi.org/10.1007/s00520-026-11297-3

Image Credits: AI Generated

DOI: 10.1007/s00520-026-11297-3

Keywords: digital symptom monitoring, patient-reported outcomes, cancer care, adolescents and young adults, eHealth, PRO-CTCAE, systemic therapy, patient engagement, implementation science, supportive care, OpenSYMPRO, health communication

The research, led by Xiomara Skrabal Ross of Canteen Australia and colleagues, tracked families in which a parent had been diagnosed with cancer within the previous twelve months. Participants were recruited through three Australian hospitals and three not-for-profit cancer organizations between May 2018 and December 2022. Each family completed quantitative questionnaires at two timepoints: once within twelve months of the diagnosis, and again six months later. The sample included 55 adolescents and young adults aged 12 to 24 years and 71 parents aged 32 to 62 years at baseline, both groups predominantly female. Of these, 31 young people and 52 parents completed both waves, allowing the researchers to chart how perceptions of family functioning changed over time. The team had also planned a third assessment one year after baseline, but high attrition left that dataset too small to analyze, a limitation the authors acknowledge candidly.

At the heart of the study is a deceptively simple question: what does it mean for a family to function well? The researchers defined family functioning as the family’s ability to deal with everyday life and cope effectively with problems and changes, and they measured it with the SCORE-15, a validated 15-item self-report instrument covering strengths and adaptability, disrupted communication, and feeling overwhelmed by difficulties. Crucially, higher scores on this scale indicate worse functioning, a detail that matters when interpreting the statistical results. The team also measured family resilience using the 54-item Family Resilience Assessment Scale, which probes domains such as family communication and problem-solving, social and economic resources, positive outlook, connectedness, spirituality, and the ability to make meaning of adversity. Psychological distress was captured with the Kessler-10, communication avoidance with the Family Avoidance of Communication About Cancer scale, unmet support needs of the young people with the Offspring Cancer Needs Instrument, and parental confidence in supporting children through cancer with the Help-Child subscale of the Cancer Self-Efficacy Scale.

The statistical approach was deliberately conservative. Separate multiple regression analyses were run for parents and for young people, controlling for age and gender, and only variables that correlated with family functioning at a threshold of p less than or equal to 0.01 were admitted into the models, keeping the number of predictors proportional to the sample size. Where more than one adolescent from the same family participated, one child was randomly selected to avoid the statistical distortion that would come from highly correlated sibling responses. The results at baseline were striking in their symmetry. For the young people, the regression model explained 62 percent of the variance in perceived family functioning; for parents, it explained 38 percent. In both groups, the same two variables emerged as significant predictors: greater family resilience and lower avoidance of communication about cancer were both associated with better family functioning within the first twelve months after diagnosis.

The numbers behind those associations are worth pausing on. Among the young people, the standardized coefficient for family resilience was β = −0.57, p < .001, and for communication avoidance β = 0.29, p = .04, with the negative sign for resilience simply reflecting the reverse scoring of the functioning scale. Among parents, the corresponding values were β = −0.21, p = .035 for resilience and β = 0.39, p < .001 for communication avoidance. In plain terms, families that talked openly about the cancer and possessed strong shared coping resources reported healthier dynamics, and this held true regardless of whether the perspective came from the parent or the child. Notably, individual psychological distress and parental self-efficacy, which correlated with functioning in the bivariate analyses, did not survive as independent predictors once resilience and communication were accounted for, suggesting that these broader family-level processes may absorb or channel the effects of individual emotional states.

Six months later, the picture shifted in an instructive way. At the second timepoint, twelve to eighteen months after diagnosis, the strongest predictor of family functioning for both groups was simply family functioning at baseline, with coefficients of β = 0.75 for the young people and β = 0.67 for the parents, both highly significant. In other words, family dynamics proved remarkably stable: families that were doing well early on continued to do well, and families that struggled early tended to keep struggling. For the young people, however, one additional factor carried independent weight. Baseline psychological distress predicted poorer perceived family functioning six months later, with β = 0.42, p = .001, even after controlling for prior functioning. This is the study’s most clinically consequential finding, because it identifies individual distress in adolescents and young adults as a sustained, forward-looking risk factor rather than a passing reaction to the crisis.

To visualize these trajectories, the researchers classified each participant using clinical cut-offs for the SCORE-15 established by Miller and colleagues, combined with a reliable change index of plus or minus nine points. Four trajectories were possible: stable high, stable low, improving, and deteriorating. The results were reassuring for the majority. Among parents, 78 percent showed stable high functioning and 16 percent stable low functioning, with only 4 percent deteriorating and 2 percent improving. Among the young people, 71 percent were stably high and 19 percent stably low, with 6 percent deteriorating and 3 percent improving. The dominant pattern, in which most families maintain healthy functioning through an aversive event with minimal lasting disruption, is precisely what resilience researchers call minimal-impact resilience, a concept drawn from the work of George Bonanno and colleagues on trajectories of adaptation after adversity.

Yet the minority trajectories carry the clinical urgency. Roughly one in five participants, between 16 and 19 percent, showed persistently low family functioning across both timepoints, and small additional fractions worsened over the study period. These families represent a clearly identifiable at-risk group for whom early intervention could be decisive. The authors argue that the stability of the trajectories itself constitutes an argument for acting early: because family functioning at baseline so strongly predicts functioning months later, the first year after diagnosis is the window in which assessment and support are most likely to alter long-term outcomes. Encouragingly, the study also found significant correlations between parents and their own children at baseline for family functioning, family resilience, and psychological distress, meaning that a clinician assessing one family member gains meaningful information about the others. Interestingly, communication avoidance did not correlate between generations, which the authors suggest may reflect differing communication needs within the same family system, with parents worrying about how to talk while young people urgently want information.

The implications reach beyond oncology wards. The findings align with a growing body of evidence that young people whose parents have cancer experience distress at levels substantially higher than their peers and report unmet needs concentrated in emotional support and family relationships. The study’s authors point to practical supports already available, such as peer programs and age-tailored counseling offered through Australian cancer not-for-profit organizations, and emphasize that identifying parent-patients in oncology settings and connecting their families to these resources can reduce distress in young people. Because distress in the young person appears to erode their perception of family wellbeing over time, treating the adolescent or young adult as a patient in their own right, rather than a bystander to a parent’s illness, may protect the entire family system.

The researchers are careful about the limits of their work. The sample was modest, particularly at follow-up, and skewed female and toward solid tumors, with breast cancer accounting for 31 of the 52 solid tumor cases, which constrains generalizability to other cancer types and to fathers and sons. The clinical cut-offs used to define trajectories were derived from adult data rather than adolescent-specific norms, and unmeasured variables such as illness severity and treatment type may also shape family functioning. The authors call for larger, more diverse samples with additional assessment points, and for qualitative research capturing the perspectives of both parents and young people across all four trajectory groups. Still, the core message stands with unusual clarity for a field often dominated by ambiguity: in the year after a parent’s cancer diagnosis, most families bend without breaking, the families that struggle tend to have been struggling from the start, and the twin levers of open communication and family resilience offer clinicians concrete, modifiable targets for helping households weather one of life’s hardest storms.

Subject of Research: Family functioning trajectories in parents with cancer and their adolescent and young adult children

Article Title: Correlates and trajectories of family functioning in parents with cancer and their adolescent and young adult children

Article References: Skrabal Ross, X., Chilton, R. P., Wang, Y., Konings, S., Weeks, N., & McDonald, F. E. J. (2026). Correlates and trajectories of family functioning in parents with cancer and their adolescent and young adult children. Supportive Care in Cancer, 34(10), Article 1038. https://doi.org/10.1007/s00520-026-11241-5

Image Credits: AI Generated

DOI: 10.1007/s00520-026-11241-5

Keywords: parental cancer, family functioning, family resilience, adolescents and young adults, communication avoidance, psychological distress, longitudinal study, psychosocial oncology, parenting self-efficacy, supportive care, Correlates, trajectories

The study enrolled 542 adolescents and young adults with mental disorders who underwent in-hospital treatment between October 2022 and December 2023. For each participant, the researchers gathered a broad set of information: clinical characteristics, scores on the Brøset Violence Checklist, a structured instrument for assessing short-term risk of aggression, and serum hormone levels measured in blood samples. The goal was to determine which of these many variables, alone or in combination, were most strongly associated with a baseline history of suicide attempt, meaning an attempt that had occurred before or at the time of admission.

To build a model that would generalize well rather than simply fit the data at hand, the team randomly divided the cohort into a training set of 379 patients and a validation set of 163 patients, in a seven-to-three ratio. All statistical model building was performed on the training set, while the validation set was reserved to test how well the resulting tool performed on patients it had never seen. This internal validation strategy is considered a cornerstone of responsible predictive modeling in clinical research, because a model that only performs well on the data used to build it is of little practical value at the bedside.

Before fitting the final model, the researchers applied least absolute shrinkage and selection operator regression, commonly known as LASSO, a technique that shrinks the coefficients of weak predictors toward zero and effectively filters out redundant or uninformative variables. LASSO regression is particularly useful in medical datasets where many candidate predictors are correlated with one another, as it reduces overfitting and produces a more parsimonious set of features. The variables that survived this screening step were then entered into a multivariate logistic regression model, the standard statistical framework for estimating the independent contribution of each factor to a binary outcome, in this case the presence or absence of a suicide-attempt history.

Five variables ultimately earned a place in the final nomogram: a history of non-suicidal self-injury, serum prolactin levels, plasma total cortisol levels, the number of previous hospitalizations, and educational level. Each of these factors carries its own clinical logic. Non-suicidal self-injury, the deliberate harming of one’s own body without suicidal intent, has long been recognized as a powerful signal of underlying emotional distress and is one of the most consistent correlates of later suicidal behavior in young people. Repeated hospitalizations suggest a chronic or unstable course of illness, while lower educational attainment may reflect the cumulative disruption that mental disorders impose on development and social functioning.

The inclusion of two hormones adds a biological dimension that sets this study apart from many purely psychosocial risk models. Prolactin, best known for its role in lactation, is also influenced by stress and by several psychiatric medications, and altered prolactin levels have been linked in previous research to self-directed violence and emotional dysregulation. Cortisol, the body’s primary stress hormone, reflects the activity of the hypothalamic-pituitary-adrenal axis, the neuroendocrine system that orchestrates the physiological response to stress. Dysregulation of this axis has repeatedly been implicated in mood disorders and suicidal behavior, and measuring plasma total cortisol offers an objective, laboratory-based window into a patient’s stress biology that cannot be captured by interview alone.

A nomogram translates the coefficients of a logistic regression model into a visual chart on which a clinician can plot the value of each predictor, read off assigned points, sum them, and convert the total into an estimated probability. In this case, the estimated probability refers to the likelihood that a given hospitalized adolescent or young adult has a baseline history of suicide attempt. The appeal of the format lies in its simplicity: no calculator or software is required, and the relative weight of each factor is immediately visible, which can help clinicians understand why a particular patient scores as high or low risk.

The performance metrics reported by the team suggest a moderately strong tool. The area under the receiver operating characteristic curve, or AUC, a measure of how well a model distinguishes between those with and without the outcome, reached 0.771 in the training set, with a 95 percent confidence interval of 0.715 to 0.827, and 0.788 in the validation set, with a confidence interval of 0.711 to 0.865. Values in this range indicate discrimination that is useful but not definitive, which the authors explicitly acknowledge. Calibration, meaning the agreement between predicted probabilities and observed outcomes, was strong: the Hosmer-Lemeshow goodness-of-fit test yielded a chi-square of 12.672 with a p-value of 0.123 in the training set and a chi-square of 8.919 with a p-value of 0.349 in the validation set, both indicating satisfactory model fit. Decision curve analysis, a method for quantifying the clinical net benefit of a model across a range of decision thresholds, identified effective thresholds from 19 percent to 73 percent in the training set and from 10 percent to 74 percent in the validation set, supporting the tool’s utility as a risk-stratification reference.

The authors are careful to position the nomogram as an auxiliary quantitative reference rather than a standalone screening instrument. Suicide risk assessment in young people is a complex clinical judgment that must integrate interview findings, collateral information, mental state examination and safety planning, and no statistical model can replace that process. What this tool offers is a structured way to visualize how multiple dimensions of a patient’s profile, from self-injury history to endocrine markers, converge to shape risk, potentially flagging high-risk patients who might otherwise receive insufficient attention during a busy admission. The researchers suggest it may help inform targeted suicide-prevention interventions within inpatient psychiatric settings.

Because the study is cross-sectional, it captures associations at a single point in time and cannot establish that the five predictors cause suicide attempts, nor can it predict future attempts prospectively. The cohort was drawn from a single hospital network in China, so external validation in other countries, cultures and healthcare systems will be needed before broader adoption. Nonetheless, the work represents a meaningful step toward integrating biological and psychosocial data into accessible bedside tools for one of medicine’s most urgent challenges. As the authors note, characterizing the risk profiles of hospitalized adolescents and young adults with mental disorders is essential for targeted suicide prevention, and a validated, visual, multidimensional model gives clinicians one more way to see that risk clearly.

Subject of Research: A nomogram integrating psychosocial and hormonal correlates of baseline suicide-attempt history in hospitalized adolescents and young adults with mental disorders

Article Title: Development and internal validation of a nomogram for visualising multidimensional correlates of baseline suicide‑attempt history among hospitalized adolescents and young adults with mental disorders

Article References: Ren, Y., Liu, Q., Zhu, Y., Wu, J., Chen, H., Pu, L., Dong, Z., Zhu, H., & Zhang, X. (2026). Development and internal validation of a nomogram for visualising multidimensional correlates of baseline suicide‑attempt history among hospitalized adolescents and young adults with mental disorders. BMC Psychiatry. https://doi.org/10.1186/s12888-026-08613-w

Image Credits: AI Generated

DOI: 10.1186/s12888-026-08613-w

Keywords: nomogram, suicide-attempt history, adolescents and young adults, mental disorders, non-suicidal self-injury, prolactin, cortisol, LASSO regression, logistic regression, risk prediction, BMC Psychiatry, inpatient psychiatry