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Record W1858946267 · doi:10.1186/s13104-015-1448-6

Rosiglitazone use and associated adverse event rates in Canada: an updated analysis

2015· article· en· W1858946267 on OpenAlexaffabout
Sandra Iczkovitz, Daniella Dhalla, Jorge Alfonso Ross Terres

Bibliographic record

VenueBMC Research Notes · 2015
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacovigilance and Adverse Drug Reactions
Canadian institutionsGlaxoSmithKline (Canada)
FundersGlaxoSmithKline
KeywordsRosiglitazoneMedicineAdverse effectEvent (particle physics)MEDLINEData scienceComputer scienceInternal medicinePolitical science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.271
Threshold uncertainty score0.726

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.465
GPT teacher head0.540
Teacher spread0.075 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2015
Admission routes2
Has abstractyes

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