Fractures are increased and bisphosphonate use decreased in individuals with insulin-dependent diabetes: a 10 year cohort study
Bibliographic record
Abstract
BACKGROUND: Individuals with diabetes have been found previously to be at increased risk of non-traumatic fracture. However, it is unclear if these individuals are being identified and treated for osteoporosis. METHODS: 7753 Canadians over 50 years of age were followed prospectively for 10 years. 606/7753 (7.8%) of had diabetes; 98 were insulin-dependent and 508 were not. Using a cox proportional hazards model, we assessed the association between diabetes status and incident non-traumatic fracture. Using logistic regression we identified factors associated with bisphosphonate use over the 10 year period of study. RESULTS: Mean (SD) age of participants was 66.7(9.4) years and 72% were female. Those with diabetes had higher BMD T-scores at baseline, with a mean (SD) femoral neck T-Score of -0.97 (1.06), compared to -1.24 (0.99) in the general cohort. The adjusted hazard ratio (HR) for incident non-traumatic fracture in individuals with insulin-dependent diabetes over the 10 year study period was 2.50 (95% confidence interval [CI] 1.60, 3.90; p < 0.001). Despite this increased fracture rate, individuals with diabetes (insulin-dependent or non-insulin-dependent) were less likely to be on bisphosphonate therapy at any point over 10 years of prospective follow up compared to other CaMos subjects (odds ratio [OR]: 0.59; 95% CI 0.46-0.75, p < 0.001). CONCLUSIONS: Despite the increased risk of non-traumatic fracture associated with insulin-dependent diabetes, we that found individuals with diabetes are less likely to be treated with a bisphosphonate than those without diabetes. These findings point to a possible care gap in the treatment of non-traumatic fractures in individuals with diabetes in Canada.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".