Rosiglitazone use and associated adverse event rates in Canada: an updated analysis
Bibliographic record
Abstract
BACKGROUND: We previously reported on the change in the use of rosiglitazone-containing products (RCP) and adverse event reporting rates in Canadian patients between 2004 and 2010. The present study extends this analysis to include the January 2011 to December 2012 time period. METHODS: RCP utilization rates were obtained from IMS Health Brogan's longitudinal de-identified patient database, LRx. GlaxoSmithKline's global adverse events database was used to extract adverse events (AE), serious adverse events (SAE), and cardiac adverse events (CAE) reported in Canadian patients receiving RCP from April 2004 to December 2012. The patient utilization information from the LRx database was used to estimate rates per 100,000 patients. RESULTS: An estimated 182,841 patients were dispensed RCP prescriptions between April 2004 and December 2012. The total number of patients using RCP decreased by 85% from 2011 to 2012. From its peak use in 2007, the number of patients filling a prescription decreased 97%. A total of 1069 AEs were reported during the study period, of which 32 AE's were reported from Jan 2011 to Dec 2012. The average monthly reporting rates of AE's, SAE's and CAE's over 2011-2012 were 10.8/100,000 patients, 9.1/100,000 patients and 5.0/100,000 patients, respectively. CONCLUSIONS: The utilization of RCP in Canada has significantly declined. The significance of the adverse event rate information presented is uncertain and must be evaluated within the context of the well known factors that can influence AE reporting rates, as well as limitations to the methods used to estimate these reporting rates.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".