The rise and fall of the thiazolidinediones: impact of clinical evidence publication and formulary change on the prescription incidence of thiazolidinediones.
Bibliographic record
Abstract
BACKGROUND: Numerous factors affect drug utilization including clinical trials, promotional activity, drug safety signals and funding practices. We sought to investigate the impact of cardiovascular safety concerns and public drug formulary restrictions on the use of the thiazolidinediones (TZDs): rosiglitazone and pioglitazone. METHODS: We conducted a population-based cross-sectional time series analysis among more than 1.6 million older residents of Ontario, Canada using administrative healthcare claims databases from January 2000 to September 2010 to examine the impact of two events on the rate of initiation of TZDs among those aged 66 years and older: 1) the publication of a prominent meta-analysis suggesting cardiovascular harm for rosiglitazone, and 2) the introduction of prescribing restrictions for TZDs on the public formulary. RESULTS: Incident rosiglitazone prescribing decreased significantly from 5.32 to 0.44 prescriptions per 1,000 patients in the quarter following the publication of a meta-analysis, suggesting safety concerns for rosiglitazone (p<0.01). Similarly, incident pioglitazone prescribing continued to decline from 1.89 just prior to the publication of the meta-analysis to 0.53 prescriptions per 1,000 patients just prior to the policy implementation (p<0.01). Following the implementation of formulary restrictions for TZDs in Q2 of 2009, the rate of incident prescriptions for rosiglitazone fell further, from 0.20 prescriptions per 1,000 patients in the preceding quarter to 0.03 prescriptions per 1,000 patients in the subsequent quarter (Q3 of 2009; p<0.01). The rate of prescriptions dispensed for pioglitazone also decreased from 0.53 in Q1of 2009 to 0.11 prescriptions per 1,000 patients in Q3 of 2009 (p <0.01). CONCLUSION: Both the publication of clinical evidence and drug policy changes can significantly influence the utilization of the TZDs.
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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 | 0.056 | 0.232 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".