Rosiglitazone use and associated adverse event rates in Canada between 2004 and 2010
Bibliographic record
Abstract
BACKGROUND: We examined the change in the use of rosiglitazone-containing products (RCPs) Canada-wide between 2004 and 2010 and whether the rates of adverse events in association with RCP therapy in Canadian patients changed in this period to better understand the real world use of RCP medications and as part of a regulatory commitment by GlaxoSmithKline to Health Canada to assess whether there was an impact of a risk communication on cardiac safety. METHODS: RCP utilization data were obtained from IMS Brogan's longitudinal de-identified patient database (known as LRx) that tracks prescription activity using store-based data collection from pharmacies in all Canadian provinces. Adverse events (AEs), serious adverse events (SAEs) and cardiac AEs associated with RCP use in Canadian patients between April 2004 and December 2010 were identified from GlaxoSmithKline's AE database and, using the LRx data, rates per 100,000 patients were estimated. RESULTS: A total of 239,184 patients were identified as having received at least one RCP prescription between 2004 and 2010 from the LRx. After excluding those with inconsistent gender or age, only one RCP prescription at the pharmacy, a prescription from a pharmacy that had not consistently reported for the past six years or an unreasonably high number of prescriptions, 180,936 patients remained for the analysis. The number of reports identified from the AE database that occurred between April 2004 and December 2010 was 1,037. The average monthly rates of AEs, SAEs and cardiac AEs decreased by 57%, 43% and 4%, respectively, between the observed periods, April 2004-October 2007 and November 2007-December 2010. CONCLUSIONS: The findings of this analysis demonstrate a significant decrease in RCP use in Canada following a meta-analysis publication suggesting harm, which has been maintained. It is not possible to disentangle whether the continuing decline can be attributed to the meta-analysis, the changes in prescribing guidelines, media attention or a combination of some or all of these factors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| 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".