Using the same bone density reference database for men and women provides a simpler estimation of fracture risk
Bibliographic record
Abstract
Although low bone mineral density (BMD) predicts fractures, there are postulated sex differences in the fracture "threshold." Some studies demonstrate a higher mean BMD for men with fractures than for women, whereas others note similar absolute risk at the same level of BMD. Our objective was to test the preceding observations in the population-based Canadian Multicentre Osteoporosis Study (CaMOS). We included participants 50+ years of age at baseline. Mean BMD in men was higher than in women among both fracture cases and noncases. Three methods of BMD normalization were compared in age-adjusted Cox proportional hazards models. In a model using the same reference population mean and standard deviation (SD), there were strong effects of age and total-hip BMD for prediction of fractures but no significant effect of sex [hazard ratio (HR) = 0.97, 95% confidence interval (CI) 0.78-1.20] for men versus women. In a model using sex-specific reference means but a common SD, an apparent sex difference emerged (HR = 0.66, 95% CI 0.54-0.81) for men versus women. The sex term in the second model counterbalanced the higher risk introduced by the lower normalized BMD in men. A third model using sex-specific reference means and SDs gave nearly identical results. Parallel results for the three methods of normalization were seen when adjusting for clinical risk factors, excluding antiresorptive users and considering death as a competing risk. We conclude that no adjustment for sex is necessary when using common reference data for both men and women, whereas using sex-specific reference data requires a substantial secondary adjustment for sex.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".