Risk Factors for Fracture in Diabetes: The Canadian Multicentre Osteoporosis Study
Bibliographic record
Abstract
Objective . Individuals with diabetes have been found to be at increased risk of nontraumatic fracture. However, within the diabetic population, how to distinguish who is at the highest risk and warranting therapy has remained elusive. Design . Cross-sectional analysis of a national population-based cohort study. Patients . Men and women over the age of 50 with diabetes from across Canada. Measurements . Logistic regression analysis to identify diabetes specific factors associated with a history of one or more non-traumatic fractures. Results . Six hundred and six individuals with diabetes with a mean age of 69 years were examined. Thirty percent had a history of non-traumatic fracture. Macrovascular diseases in the form of stroke or TIA, as well as hypertension, were found to be independently associated with fragility fracture. Other, more traditional, clinical risk factors were also associated with fracture, including increased age, female gender, rheumatoid arthritis, family history of osteoporosis, and decreased bone mineral density. Conclusions . In this cohort of Canadians with diabetes, those with rheumatoid arthritis, a family history of osteoporosis, female gender, increased age, decreased BMD, cerebrovascular disease, or hypertension were more likely to have had a non-traumatic fracture. These risk factors may be important to clinicians when identifying which of their diabetic patients are at highest risk of fracture and in need of preventative therapies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".