Sodium–Glucose Cotransporter 2 Inhibitors and the Risk of Fractures Among Patients With Type 2 Diabetes
Bibliographic record
Abstract
The association between sodium–glucose cotransporter 2 (SGLT2) inhibitors and the risk of fractures is controversial. In the Canagliflozin Cardiovascular Assessment Study (CANVAS), canagliflozin was associated with a significant increased risk of fractures compared with placebo (hazard ratio [HR] 1.26, 95% CI 1.04–1.52) (1). Possible mechanisms may involve elevated serum phosphate levels or reductions in bone mineral density (2). To date, three recent observational studies investigated this association, but these did not observe an increased risk of fractures (3–5). However, these studies had some limitations, including residual confounding, a limited outcome definition, or restriction to a single SGLT2 inhibitor. To address these limitations, we conducted a population-based cohort study using the U.K. Clinical Practice Research Datalink (CPRD). We first assembled a base cohort of all individuals, at least 40 years old, newly treated with antidiabetic drugs between 1 January 1988 and 31 December 2017. We excluded individuals with <1 year of medical history, those initially prescribed insulin in monotherapy, and women with polycystic ovary syndrome (alternate metformin indication) at the time of the first-ever prescription of an antidiabetic drug. Using this base cohort, we then assembled a study cohort of individuals who initiated a new antidiabetic drug class as of 2013 (year the first SGLT2 inhibitor was introduced …
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".