Repeat Low-Trauma Fractures Occur Frequently Among Men and Women Who Have Osteopenic BMD
Bibliographic record
Abstract
Fracture risk assessment based solely on BMD has limitations. Additional risk factors include the presence of a previous low-trauma fracture. We sought to quantify the fracture burden attributable to first versus repeat fracture. We studied 2179 men and 5269 women, 50-90 yr of age, participating in the Canadian Multicentre Osteoporosis Study (CaMos). We included all low-trauma fractures that occurred over 8 yr of follow-up and classified these as either first or repeat clinical low-trauma fracture based on lifetime fracture history. Analyses were further stratified by sex, age, BMD risk categories (normal, osteopenia, osteoporosis), and vertebral deformity status. There were 128 fractures in men and 577 fractures in women. About 25% of fractures in men and 40% in women were repeat fractures. Just over one half of first fractures occurred in those with osteopenic BMD (58% in men, 54% in women). Just under one half of repeat fractures also occurred in those with osteopenic BMD (42% in men, 47% in women). The incidence of repeat fracture was, in most cases, nearly double, but sometimes nearly quadruple, the incidence of first fracture within a given BMD risk category in both men and women. Repeat fractures contribute substantially to overall fracture burden, and the contribution is independent of BMD. Furthermore, those with a combination of prior low-trauma fracture and another risk factor were at especially high risk of future fracture.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".