The first study focused on stated forwarding intentions. A total of 266 Chinese participants read three pairs of health-related messages, each pair consisting of a piece of misinformation and a matched detailed correction. In one condition, participants rated how likely they would be to forward the correction directly. In another, they were instructed to assume that the misinformation had already been forwarded before they saw the correction, and then rated their willingness to forward the corrective message. Under the conditions used, in which labels disclosing veracity were not identical across message types, direct corrections actually elicited a higher stated forwarding likelihood than misinformation, with a mean difference of 1.24 on the rating scale, a 95 percent confidence interval of 0.52 to 1.96, and an effect size of Hedges’ g equal to 0.63. Telling participants that the misinformation had already spread did not significantly reduce their stated willingness to forward the correction; the mean difference was 0.35, with a confidence interval spanning from minus 0.37 to 1.07. The authors are careful to note, however, that these are hypothetical intentions, vulnerable to social desirability bias and to the well-documented gap between what people say they will do and what they actually do. Because the labeling of messages differed across conditions, the design also cannot fully isolate the effect of correction content itself from the effects of disclosed veracity or corrective framing.

The second study translated these measured propensities into a competitive agent-based model of diffusion. The researchers used the survey ratings as a relative propensity profile rather than as calibrated probabilities for any real platform, and ran simulated contests between misinformation and corrections across three classic network structures: Barabási–Albert scale-free networks, Erdős–Rényi random networks, and Watts–Strogatz small-world networks. They systematically varied four factors: transmission intensity, the onset delay before corrections entered, the supply of correction seeds, and the placement of those seeds in the network. The central quantity was the temporal catch-up cost, abbreviated TCC, defined as the smallest tested ratio of correction seeds to misinformation seeds that restores the cumulative-coverage reduction achieved when both message types start seeding simultaneously in equal numbers, minus one. In plain terms, it measures how many extra corrective seeds are needed to make up for a delayed start.

The simulation results were unambiguous about the price of delay. Across three transmission regimes, all 18 equal-seeding delay penalties were positive, and each penalty grew larger as the delay lengthened and as transmission intensity increased. When corrections were allowed to enter after only one generation of misinformation spread, all nine combinations of regime and network topology managed to recover the benchmark coverage within the tested parameter grid, with temporal catch-up costs ranging from 0.80 to 7.60. After two generations of delay, however, outcomes diverged sharply: some combinations still recovered within the grid, while others failed to do so entirely. Most strikingly, for Barabási–Albert networks under the reference regime, the model implied that no tested seed ratio could restore the benchmark at all, a condition the authors describe as model-implied unattainability.

The one encouraging result involved placement strategy: targeting high-degree nodes in the reference regime especially reduced the catch-up requirements on Barabási–Albert networks, indicating that in the model, where corrections are seeded changed how many were needed.

The authors frame their conclusions with deliberate caution. The thresholds they report are conditional comparisons within their model, not estimates of the resources or clock time that any specific platform would need to contain a real rumor outbreak. The survey findings rest on self-reported intentions rather than observed sharing behavior, and the labeling asymmetry across conditions limits what the first study can prove about correction content alone. What the combined approach does establish is a conceptual point: a correction-sharing disadvantage is not necessary for corrections to lose at the network level. Even when people report being more willing to forward corrections than misinformation, the timing of a correction’s entry can generate catch-up costs that grow steeply with delay, and in some simulated regimes become impossible to overcome regardless of how many corrective seeds are committed.

Subject of Research: The timing disadvantage of misinformation corrections in network diffusion, studied through forwarding-intention surveys and agent-based modeling

Article Title: From sharing propensity to diffusion control: temporal catch-up costs of delayed misinformation correction

Article References: Ding, W., Cheng, Z., & Ge, Y. (2026). From sharing propensity to diffusion control: temporal catch-up costs of delayed misinformation correction. Frontiers in Physics, 14, Article 1953841. https://doi.org/10.3389/fphy.2026.1953841

Image Credits: AI Generated

DOI: 10.3389/fphy.2026.1953841

Keywords: misinformation, fact-checking, information diffusion, agent-based modeling, complex networks, social physics, correction timing, temporal catch-up cost, seed placement, forwarding intentions, sharing, propensity

Community-acquired pneumonia complicated by sepsis remains one of the most demanding infectious syndromes in acute care, carrying substantial morbidity, mortality, and healthcare burden. Clinical guidelines widely recommend beta-lactam-based combination therapy for severe pneumonia, yet the optimal empirical regimen for septic pneumonia specifically has remained uncertain. Clinicians face a persistent balancing act: starting early treatment broad enough to cover likely pathogens while avoiding unnecessary broad-spectrum antibiotic use that fuels antimicrobial resistance. Evidence directly addressing this high-risk population has been limited, which motivated the analysis.

The study enrolled adult patients admitted between January 2021 and January 2025 who had community-acquired pneumonia-associated sepsis and received empirical therapy within 48 hours of admission. Patients were assigned to one of two strategies: beta-lactam monotherapy, or beta-lactam-based combination therapy with a macrolide or doxycycline. Before statistical matching, 100 patients were included; after 1:1 propensity score matching, which paired patients with comparable baseline characteristics, 80 patients remained for analysis.

The primary outcome was time to clinical stability (TTCS). Secondary outcomes included mortality, length of hospital stay, antibiotic duration, changes in Sequential Organ Failure Assessment (SOFA) scores, inflammatory biomarkers, clinical cure, and adverse events. Time to clinical stability was analyzed with Kaplan-Meier curves and Cox proportional hazards regression.

Median time to clinical stability was 5.0 days in the monotherapy group and 4.0 days in the combination therapy group, a one-day difference that was not statistically significant (P=0.129). Combination therapy also showed favorable trends in hospitalization duration, clinical cure, reduction of inflammatory markers, and recovery of organ function, though the reported results did not establish statistically significant differences for these outcomes in this sample.

The investigators adjusted for admission SOFA score in their regression model. After this adjustment, combination therapy showed a nonsignificant trend toward earlier clinical stability, with an adjusted hazard ratio of 1.42 (95% confidence interval 0.88-2.29; P=0.151). A hazard ratio above 1 indicates a higher probability of reaching stability at any given time, but the confidence interval crossing 1.0 means the data cannot rule out no difference.

No significant increase in adverse events was observed in the cohort, an observation the authors report alongside the favorable but nonsignificant efficacy trends.

The authors conclude that in patients with community-acquired pneumonia-associated sepsis, beta-lactam-based combination therapy showed a trend toward earlier clinical stability than beta-lactam monotherapy, although the difference was not statistically significant. They frame the work as addressing an important evidence gap in septic pneumonia management and as supporting further investigation of individualized antimicrobial strategies guided by disease severity and biological characteristics, an approach aligned with the broader push toward precision antimicrobial management.

The interpretation carries important caveats that the authors themselves emphasize. The retrospective, single-center design means treatment assignment reflected real-world clinical practice rather than randomization, and unmeasured differences between patients could influence outcomes even after propensity matching. The relatively small sample size of 80 matched patients limits statistical power, so a true moderate benefit of combination therapy could plausibly have produced the observed pattern without reaching significance. The authors state that these limitations should be considered when interpreting the findings, and the results support larger, ideally prospective, studies before any change to empirical prescribing practice for septic pneumonia.

Subject of Research: Clinical outcomes of empirical antibiotic strategies in community-acquired pneumonia-associated sepsis: a propensity score-matched cohort study

Article Title: Clinical outcomes of empirical antibiotic strategies in community-acquired pneumonia-associated sepsis: a propensity score-matched cohort study

Article References: Ding, L., Hu, R., & Ying, Y. (2026). Clinical outcomes of empirical antibiotic strategies in community-acquired pneumonia-associated sepsis: a propensity score-matched cohort study. Frontiers in Cellular and Infection Microbiology, 16, Article 1979459. https://doi.org/10.3389/fcimb.2026.1979459

Image Credits: AI Generated

DOI: 10.3389/fcimb.2026.1979459

Keywords: Clinical, outcomes, empirical, antibiotic, strategies, community-acquired, pneumonia-associated, sepsis, propensity, score-matched, cohort, scientific research