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Record W7095723251

Analysis CMAJ

2012· article· en· W7095723251 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsHarmPublic healthClinical trialScientific evidenceHealth careConfidentialityLiabilityStrengths and weaknesses
DOInot available

Abstract

fetched live from OpenAlex

Health Canada should publicly discloseinformation about the safety and effi-cacy of pharmaceuticals, biologics and medical devices, and should especially disclose the designs and results of clinical trials. This disclosure is necessary to preserve public trust,1 address weaknesses in the evidence base2 and protect Canadians from harm.3 A prime example of the need for this disclosure involves selective serotonin reuptake inhibitors (SSRIs). Health Canada did not authorize SSRIs for sale to people younger than 19 years because of data from clinical trials showing risks of harm, including self-harm, associated with use of SSRIs in that age group. But Health Canada also did not publicly disclose that evidence, and by 2004 SSRIs were being widely prescribed for teenagers. Physi-cians had no idea they were invoking their discre-tion to prescribe “off label ” on the basis of incom-plete information — the balance of which Health Canada had in hand.4,5 Assessing how often harm results from nondis-closure is difficult because reporting of adverse events remains poor.6 What is clear from several analyses is that there is often a chasm between the published scientific literature (which is biased toward positive results) and the information that regulators possess about a given drug.2,7,8 Why does Health Canada not divulge infor-mation from clinical trials until reports surface of widespread off-label prescribing? The reason is legal: the companies that manufacture these ther-apeutic products and devices claim that informa-tion is “confidential business information ” or a “trade secret, ” which they own, and which Health Canada is not free to disclose. I witnessed this pas de deux while attending Health Canada’s “technical discussions on regu-latory modernization ” held between October 2010 and January 2011. Each proposal put on the table by Health Canada to increase trans-parency — from making final decisions regard-ing applications for market authorization pub-licly available, to creating an online register of therapeutic products — was met with proprietary claims from MEDEC, BIOTECanada or Rx&D, the respective associations of medical device, biotechnology and pharmaceutical companies in

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.432
Threshold uncertainty score0.616

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.5680.192

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 head0.609
GPT teacher head0.617
Teacher spread0.008 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2012
Admission routes1
Has abstractyes

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