Implications of expanding indications for drug treatment to prevent fracture in older men in United States: cross sectional and longitudinal analysis of prospective cohort study
Bibliographic record
Abstract
OBJECTIVES: To quantify incremental effects of applying different criteria to identify men who are candidates for drug treatment to prevent fracture and to examine the extent to which fracture probabilities vary across distinct categories of men defined by these criteria. DESIGN: Cross sectional and longitudinal analysis of a prospective cohort study. SETTING: Multicenter Osteoporotic Fractures in Men (MrOS) study in the United States. PARTICIPANTS: 5880 untreated community dwelling men aged 65 years or over classified into four distinct groups: osteoporosis by World Health Organization criteria alone; osteoporosis by National Osteoporosis Foundation (NOF) but not WHO criteria; no osteoporosis but at high fracture risk (at or above NOF derived FRAX intervention thresholds recommended for US); and no osteoporosis and at low fracture risk (below NOF derived FRAX intervention thresholds recommended for US). MAIN OUTCOME MEASURES: Proportion of men identified for drug treatment; predicted 10 year probabilities of hip and major osteoporotic fracture calculated using FRAX algorithm with femoral neck bone mineral density; observed 10 year probabilities for confirmed incident hip and major osteoporotic (hip, clinical vertebral, wrist, or humerus) fracture events calculated using cumulative incidence estimation, accounting for competing risk of mortality. RESULTS: 130 (2.2%) men were identified as having osteoporosis by using the WHO definition, and an additional 422 were identified by applying the NOF definition (total osteoporosis prevalence 9.4%). Application of NOF derived FRAX intervention thresholds led to 936 (15.9%) additional men without osteoporosis being identified as at high fracture risk, raising the total prevalence of men potentially eligible for drug treatment to 25.3%. Observed 10 year hip fracture probabilities were 20.6% for men with osteoporosis by WHO criteria alone, 6.8% for men with osteoporosis by NOF (but not WHO) criteria, 6.4% for men without osteoporosis but classified as at high fracture risk, and 1.5% for men without osteoporosis and classified as at low fracture risk. A similar pattern was noted in observed fracture probabilities for major osteoporotic fracture. Among men with osteoporosis by WHO criteria, observed fracture probabilities were greater than FRAX predicted probabilities (20.6% v 9.5% for hip fracture and 30.0% v 17.4% for major osteoporotic fracture). CONCLUSIONS AND RELEVANCE: Choice of definition of osteoporosis and use of NOF derived FRAX intervention thresholds have major effects on the proportion of older men identified as warranting drug treatment to prevent fracture. Among men identified with osteoporosis by WHO criteria, who comprised 2% of the study population, actual observed fracture probabilities during 10 years of follow-up were highest and exceeded FRAX predicted fracture probabilities. On the basis of findings from randomized trials in women, these men are most likely to benefit from treatment. Expanding indications for treatment beyond this small group has uncertain value owing to lower observed fracture probabilities and uncertain benefits of treatment among men not selected on the basis of WHO criteria.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".