MétaCan
Menu
Back to cohort
Record W2311899267

New drug approval times and safety warnings in the United States and Canada, 1992-2011.

2013· article· en· W2311899267 on OpenAlexaffabout
Nigel S. B. Rawson

Bibliographic record

VenuePubMed · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsToronto East General Hospital
Fundersnot available
KeywordsMedicineDrug approvalDrugPharmacology
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: New drug approvals in the US and Canada were reviewed in short-term studies in the 1990s. A database of drugs approved in both countries between 1992 and 2011 exists allowing for a longer time horizon to assess trends. OBJECTIVE: To compare review times of drugs approved in the US and Canada over the 20-year period and their duration on the respective markets until any serious safety risk arose. METHODS: Data on submission and approval dates and review type were obtained from the regulatory agencies. RESULTS: 454 drugs were approved in both countries in the 20-year period for which the US median approval time was shorter than the Canadian median by >6 months (382 versus 574 days). Nevertheless, in 2007-11, the median approval times were closer in the two countries (302 and 356 days, respectively). 3% of the drugs were discontinued for safety reasons in both countries. The 10-year survival rate without a serious safety warning was significantly lower in Canada (58.4%) than in the US (69.3%). Being approved in 2002-11 with a shorter review time had the greatest impact on a drug receiving a serious safety warning. CONCLUSIONS: Overall, new drug approval times in the two countries in the last five years were closer, although some important differences remain so that Canadians still wait longer for some new drugs to be approved. The survival rate of a drug without a serious warning decreased substantially in the last decade in both countries, especially in drugs approved with shorter review times.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.695
Threshold uncertainty score0.854

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.022
GPT teacher head0.198
Teacher spread0.176 · 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 designNot applicable
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

Citations15
Published2013
Admission routes2
Has abstractyes

Explore more

Same venuePubMedSame topicPharmaceutical Economics and PolicyFrench-language works237,207