Determining whether women with osteopenic bone mineral density have low, moderate, or high clinical fracture risk
Bibliographic record
Abstract
OBJECTIVE: Most low-trauma fractures occur among women with osteopenic bone mineral density (BMD), a population considered to have moderate absolute fracture risk. Our purpose was to refine the fracture risk prediction in women with osteopenic BMD to determine the subgroups at lowest and highest risk. METHODS: We included 2,588 women aged 50 to 90 years with osteopenic BMD (femoral neck BMD between -1 and -2.5) participating in the Canadian Multicentre Osteoporosis Study, an ongoing prospective cohort study of randomly selected Canadians. Baseline variables, in addition to known risk factors, age, and BMD, were considered for inclusion in a model for the prediction of 5-year absolute risk of low-trauma fracture. Models were derived using logistic regression and assessed by the Bayesian Information Criterion. RESULTS: We found an increased fracture risk among those with lower BMD (odds ratio [OR], 1.53; 95% CI, 1.06-2.21) for each decrease in femoral neck T score (eg, from -1 to -2), those with prior low-trauma fracture (OR, 2.06; 95% CI, 1.46-2.92), those with self-reported worse general health (OR, 1.35; 95% CI, 1.15-1.59) for each lower category (categories: excellent, very good, good, fair, poor), and those with height loss (OR, 1.44; 95% CI, 1.16-1.90) for each 5-cm difference between current and maximal height. The new model had yielded a better risk stratification than did a model with World Health Organization risk factors. CONCLUSIONS: Including risk factors such as general health and height loss can be used to provide a highly effective assessment of fracture risk among women with osteopenic BMD.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".