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Record W2151160448 · doi:10.1503/cmaj.050971

Is there still a role for spontaneous reporting of adverse drug reactions?

2006· article· en· W2151160448 on OpenAlexaffvenue
Joel Lexchin

Bibliographic record

VenueCanadian Medical Association Journal · 2006
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacovigilance and Adverse Drug Reactions
Canadian institutionsYork University
Fundersnot available
KeywordsDrug reactionAdverse effectAdverse drug reactionDrugMedicineIntensive care medicineComputer sciencePharmacology

Abstract

fetched live from OpenAlex

In an age of large, linked databases, is there still a role for a system of spontaneous reporting of adverse drug reactions? The problems associated with reports are well-known: poor quality of submitted reports; significant underreporting of adverse reactions; difficulty in calculating 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 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)0.653
metaresearch head score (Gemma)0.777
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.347
Threshold uncertainty score0.428

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.6530.777
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0070.005
Bibliometrics0.0170.019
Science and technology studies0.0040.011
Scholarly communication0.0290.042
Open science0.0160.014
Research integrity0.0080.015
Insufficient payload (model declined to judge)0.0060.004

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 head0.041
GPT teacher head0.379
Teacher spread0.338 · 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
DomainMethods
GenreCommentary

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

Citations61
Published2006
Admission routes2
Has abstractyes

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