Thiazolidinediones and the risk of incident strokes in patients with type 2 diabetes: a nested case‐control study
Bibliographic record
Abstract
PURPOSE: To determine whether the use of thiazolidinediones (TZDs) decreases the risk of incident strokes in patients with type 2 diabetes. METHODS: We conducted a nested case-control study within a population-based cohort from the UK General Practice Research Database (GPRD). The cohort comprised patients over the age of 40 who were prescribed a first oral hypoglycemic agent between 1 January 1988 and 30 June 2008. Cases included all subjects who experienced a first stroke during follow-up. Up to 10 controls were matched to each case on age (+/-2 years), sex, date of cohort entry (+/-1 year), and duration of follow-up. Rate ratios (RRs) of stroke associated with TZD use, including rosiglitazone and pioglitazone, relative to combinations of other oral hypoglycemic agents, were estimated using conditional logistic regression. RESULTS: The cohort comprised 75 717 users of oral hypoglycemic agents, of whom 2417 had a stroke during follow-up. The rate of stroke in users of TZDs given as monotherapy (RR: 1.20, 95%CI: 0.77, 1.86) or in combination with other oral hypoglycemic agents (RR: 0.78, 95%CI: 0.58, 1.04) was not lower than combinations of other oral hypoglycemic agents. The RRs were similar for rosiglitazone and pioglitazone. CONCLUSIONS: The results of this study indicate that TZDs do not appear to decrease the incidence of first strokes.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".