New use of rosiglitazone decreased following publication of a meta‐analysis suggesting harm
Bibliographic record
Abstract
AIMS: It is uncertain whether meta-analyses lead to changes in prescribing practices. We studied trends in the prescribing of glucose-lowering therapy before and after the publication of a meta-analysis suggesting harm from rosiglitazone. METHODS: We examined the prescription records of all residents of Ontario, Canada, aged > or = 66 years. For each week between January and December 2007, we identified new users of five categories of glucose-lowering medications: rosiglitazone, pioglitazone, metformin, glibenclamide (glyburide) and insulin. The effect of the meta-analysis was assessed using interventional autoregressive integrated moving-average models. RESULTS: Following the release of the meta-analysis, there was a sudden decline in new users of rosiglitazone (P = 0.01), mirrored by a nearly identical but transient increase in new users of pioglitazone (P < 0.001). There was also a net decline in new users of thiazolidinediones as a class (P < 0.001). The number of new users of other glucose-lowering medications did not change. CONCLUSIONS: A highly-publicized meta-analysis regarding rosiglitazone's potential harms led to an abrupt decline in new users of the drug, as well as a transient surge in new use of pioglitazone.
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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 | 0.046 | 0.145 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.028 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| 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".