Sex‐ and Age Group‐Specific Fracture Incidence Rates Trends for Type 1 and 2 Diabetes Mellitus
Bibliographic record
Abstract
ABSTRACT The incidence of major osteoporotic fractures has declined in men and women in Western countries over the last two decades. Although fracture risk is higher in persons with diabetes mellitus, trends of fractures remain unknown in men and women with diabetes. We investigated the trends in fracture incidence rates (IRs) in men and women with type 1 diabetes mellitus (T1D) and type 2 diabetes mellitus (T2D) in Denmark between 1997 and 2017. We identified men and women aged 18+ years who sustained a fracture (excluding skull and facial fractures) between 1997 and 2017 using the Danish National Patient Registry. We calculated sex‐specific IRs of fractures per 10,000 person‐years separately in persons with T1D, T2D, or without diabetes. Furthermore, we compared median IRs of the first 5 years (1997–2002) to the median IRs of the last 5 years (2012–2017). We identified 1,235,628 persons with fractures including 4863 (43.6% women) with T1D, 65,366 (57.5% women) with T2D, and 1,165,399 (54.1% women) without diabetes. The median IRs of fractures declined 20.2%, 19.9%, and 7.8% in men with T1D, T2D, and without diabetes, respectively ( p ‐trend <0.05). The median IRs decreased 6.4% in women with T1D ( p ‐trend = 0.35) and 25.6% in women with T2D ( p ‐trend <0.05) but increased 2.3% in women without diabetes ( p ‐trend = 0.08). Fracture IRs decreased in men with both diabetes types and only in women with T2D, highlighting the need for further attention behind the stable trend observed in women with T1D. © 2023 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".