Simple Computer Model for Calculating and Reporting 5-Year Osteoporotic Fracture Risk in Postmenopausal Women
Bibliographic record
Abstract
PURPOSE: To devise, validate, and test a software model that improves how clinicians calculate individual risk for osteoporotic fracture and expected treatment benefit. METHODS: We developed a simple model of seven easily ascertained items plus bone mineral density (BMD) that calculates absolute fracture risk and expected absolute risk reduction after treatment. Baseline clinical variables and longitudinal fracture data from two large osteoporosis cohort studies validated the model's accuracy in predicting fracture risk. We then surveyed 298 clinicians to evaluate the likelihood they would prescribe alendronate in three hypothetical cases, first given the clinical data alone and then with model-derived data on fracture risk and expected treatment benefit. RESULTS: We found a strong linear relationship with the model's predicted fracture risk and observed fracture rates in two large observational cohorts but the model overestimated risk 2-3 fold. The model predicted a 1:200 5-year risk for spinal fracture and a 1:40 risk for nonspinal fracture in an index case of a younger, thin, osteopenic woman. Given this hypothetical history with BMD t-scores, 26% of clinicians were likely to prescribe alendronate; when also given model-calculated 5-year fracture risks with or without treatment, only 13% were likely to prescribe alendronate (p < 0.001). For 2 other osteoporosis patients in whom risk was much higher, further information on fracture risk and expected treatment benefit did not alter prescribing. CONCLUSIONS: Reporting absolute fracture risk with and without treatment promises to be most useful in women with osteopenia, a common clinical dilemma in younger postmenopausal women.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".