Three of the world’s most widely used fracture-prediction tools have delivered an unusually consistent and unsettling verdict: a strikingly large share of postmenopausal women evaluated in a new Polish study face a 10-year probability of breaking a bone high enough to warrant preventive treatment. The research, published in the journal Archives of Osteoporosis on 29 August 2026, applied the FRAX, Garvan, and POL-RISK algorithms to 508 postmenopausal women recruited from three osteoporosis outpatient clinics and found that, depending on which calculator was used, between 58.5 and 73.5 percent of them crossed the therapeutic thresholds that guidelines use to identify candidates for anti-osteoporosis therapy. The hip fracture numbers were the most alarming of all. By FRAX, 185 women — 36.4 percent of the cohort — carried a 10-year hip fracture probability of 3 percent or more, the level conventionally labeled high, and 119 women, or 23.4 percent, exceeded the very-high-risk bar of 4.5 percent. The Garvan algorithm, which tends to estimate more aggressively, placed the cohort’s mean 10-year hip fracture risk at 13.33 percent — more than four times the FRAX average of 3.23 percent.

The findings land at a moment when the arithmetic of aging is turning osteoporosis from a specialty concern into a public health problem that health systems can no longer afford to underestimate. Poland, like much of Europe, is graying rapidly, and the authors frame their work against national demographic data showing a growing population of postmenopausal women — precisely the group in which fragility fractures concentrate. Osteoporosis itself is deceptively quiet: bone mineral is lost without symptoms until a vertebra collapses or a hip gives way in a fall from standing height. Hip fractures are the costliest and most feared outcome, routinely triggering hospitalization, surgery, lasting disability, and elevated mortality in the months that follow. Yet the study’s broader context may be its most troubling dimension. A multi-country European study published in 2021 documented a persistent treatment gap — a large fraction of patients who meet risk criteria never receive therapy — and research in Austria and elsewhere has found that even surviving an osteoporotic fracture often fails to trigger appropriate treatment. In other words, the tools to predict fractures exist; the will and the systems to act on them frequently do not.

The new study was straightforward in design but unusually thorough in its comparison. A team led by corresponding author Rafał Hebel, with investigators including Wojciech Pluskiewicz, Piotr Adamczyk, Bogna Drozdzowska, and Hanna Hüpsch, largely affiliated with the Medical University of Silesia, enrolled 508 consecutive postmenopausal women attending three osteoporosis outpatient clinics. Consecutive recruitment — taking every eligible patient in sequence rather than selectively — reduces cherry-picking within the clinic setting. The women’s mean age was 69.8 years, with a standard deviation of 7.5, placing most participants squarely in the age band where fracture risk accelerates. Each participant provided a structured history of clinical risk factors: prior fractures, parental hip fracture history, smoking, glucocorticoid use, and the other variables the algorithms ingest. Bone status was measured at the hip with dual-energy X-ray absorptiometry using a Lunar Prodigy device — the densitometric standard that quantifies bone mineral density at the femoral neck, the site whose geometry and mineral content make it the critical weak point in age-related fracture. Armed with these inputs, the researchers computed each woman’s 10-year fracture risk with all three algorithms and then examined how well the tools agreed.

The three calculators represent three distinct philosophies of fracture prediction. FRAX, developed under the auspices of the World Health Organization at the University of Sheffield and described in its canonical form by Kanis and colleagues in 2008, integrates a panel of clinical risk factors — with or without femoral neck bone density — into a 10-year probability of hip fracture and of major osteoporotic fracture, a composite covering the spine, hip, forearm, and upper arm. Its distinguishing feature is calibration: probabilities are adjusted to country-specific fracture incidence and mortality, so the same risk profile yields different numbers in different health systems. The Garvan algorithm, built at Australia’s Garvan Institute from long-running osteoporosis epidemiology cohorts and formalized in nomograms by Nguyen and colleagues in 2007 and 2008, takes a different route: it explicitly incorporates falls — how many a woman has suffered in the past year — alongside age, weight, prior fractures, and bone density, and it outputs absolute risk of any fragility fracture as well as hip fracture over five and ten years. POL-RISK is the homegrown entrant, derived by the same Polish research group from their prospective RAC-OST-POL cohort and published in 2023; it is calibrated specifically to postmenopausal Polish women and expresses 10-year fracture risk for that population.

On average, the tools painted a consistently grim but numerically divergent picture. FRAX returned a mean 10-year risk of major osteoporotic fracture of 8.85 percent, with a standard deviation of 5.43, and a mean hip fracture risk of 3.23 percent, with a standard deviation of 3.72. Garvan’s mean 10-year risk of any fracture was 29.71 percent, with a standard deviation of 20.53, and POL-RISK, which like Garvan estimates any-fracture risk, was close behind at 28.1 percent, with a standard deviation of 14.85. Part of the gap is definitional rather than substantive: FRAX’s major category counts only four skeletal sites, while Garvan and POL-RISK estimate the risk of breaking any bone in a fragility event, so their averages would run higher even if the underlying models agreed perfectly. Part of it is philosophical: Garvan’s explicit weighting of falls and multiple prior fractures tends to inflate estimates in women who have them, and its hip fracture output — a mean of 13.33 percent against FRAX’s 3.23 percent — illustrates how differently two respected models can score the same woman. The wide standard deviations, particularly the 18.30 percent spread around Garvan’s hip estimate, signal a cohort spanning enormous heterogeneity, from women still years away from danger to those at imminent risk.

Translated into the binary language of clinical decision-making, the numbers become more striking. Using FRAX, 159 women — 31.3 percent of the cohort — crossed the 10 percent threshold marking high risk of major osteoporotic fracture, and 64 women, 12.6 percent, exceeded the very-high-risk mark of 15 percent. When the researchers applied the therapeutic thresholds embedded in Polish and European guidance across all three tools, the verdicts converged on a sobering majority: 316 women, or 62.2 percent, were flagged as high risk by FRAX’s major fracture output, 374 women, or 73.5 percent, by POL-RISK, and 298 women, or 58.5 percent, by Garvan. Read together, the message is hard to escape. Even the most conservative of the three calculators — the one that flags the fewest women — identifies nearly three in five patients as candidates for anti-osteoporosis therapy, and the most permissive identifies nearly three in four. For a condition whose fractures are, by definition, largely preventable with existing drugs, those proportions describe not a marginal shortfall in care but a structural one.

How well did the algorithms agree when applied to the same women? The researchers computed correlation coefficients across the cohort and found a hierarchy of concordance that is itself informative. Garvan and POL-RISK tracked each other almost in lockstep, with a correlation of r = 0.93 — a relationship so tight that the two tools are nearly interchangeable in how they rank patients. FRAX, by contrast, correlated more loosely with both: r = 0.66 against Garvan and r = 0.60 against POL-RISK, all associations statistically significant at p < 0.001. For hip fracture specifically, FRAX and Garvan hip-risk estimates correlated at r = 0.57. The pattern has a technical explanation rooted in the models’ architecture. Garvan and POL-RISK share the any-fracture endpoint, treat falls and prior fractures in overlapping ways, and draw on similar statistical lineages, whereas FRAX optimizes a narrower four-site outcome, adjusts for competing mortality, and handles falls only indirectly. In practice, this divergence means a real patient can be classified as high risk by one calculator and moderate by another — a discrepancy clinicians must resolve through judgment, and one reason the authors argue for understanding each tool’s proper domain rather than treating any single output as gospel.

The authors’ conclusion is pointed: in the group studied, fracture risk — especially hip fracture risk — is relatively high, and it is advisable to expand the population of postmenopausal women eligible for effective anti-osteoporosis therapy. That phrasing is a quiet challenge to current practice. Earlier work by the same team, including a 2024 comparison of the three algorithms in the GO study cohort, and studies from other countries have repeatedly shown that a large share of high-risk women receive no treatment at all, and that care pathways matter: patients managed in specialized osteoporosis clinics tend to be evaluated and treated more reliably than those cared for solely in general practice. The Polish National Health Fund’s own reporting on osteoporosis, which the authors cite, documents the scale of the national burden. What this new analysis adds is a quantified warning about the future: if hundreds of women presenting at just three clinics carry this much accumulated 10-year risk, the projected fracture burden — and the surgical, hospital, and long-term care costs that follow each broken hip — will climb unless treatment coverage expands substantially. The study thus reframes osteoporosis not as an inevitable tax on aging but as a widening gap between what risk calculators can see and what health systems actually act upon.

The study has limits that its design makes clear. The cohort consisted of women already attending osteoporosis outpatient clinics, so their risk profile likely exceeds that of the general postmenopausal population — this is a portrait of the treated end of the iceberg, not a community survey. The analysis is also cross-sectional: it compares predicted risks rather than tracking which women actually fracture over the coming decade, although all three algorithms were themselves validated in prospective cohorts, including the Polish RAC-OST-POL study from which POL-RISK emerged. Still, the convergence is the story. Three tools built on different continents, from different cohorts, with different statistical machinery — one calibrated to British epidemiology, one to Australian, one to Polish — looked at the same 508 women and agreed that a majority belong in the treatment conversation. For the millions of postmenopausal women who have never had a bone density scan, let alone a formal risk calculation, the findings carry a simple, unglamorous takeaway: the decade ahead can be forecast today with a questionnaire and a DXA scan, and forecasting it is the first step toward changing it.