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Record W2165291399 · doi:10.1503/cmaj.071366

Serious adverse events in academic critical care research

2008· article· en· W2165291399 on OpenAlexafffundvenue
François Lauzier, Marcelo Rocha, Melissa Sayles, Simon Finfer

Bibliographic record

VenueCanadian Medical Association Journal · 2008
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsMcMaster University
FundersUniversité Laval
KeywordsAdverse effectClinical trialIdeal (ethics)Computer scienceIntensive care medicineMedicineAlternative medicineMEDLINEData sciencePharmacologyPathologyPolitical science

Abstract

fetched live from OpenAlex

Although it might be ideal to have a single system for defining and reporting serious adverse events that would be applicable to both academic trials investigating established drugs and industry-sponsored trials of novel pharmaceutical agents, such a system would be unlikely to satisfy all

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)0.364
metaresearch head score (Gemma)0.543
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.995
Threshold uncertainty score0.785

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3640.543
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0110.012
Science and technology studies0.0040.005
Scholarly communication0.0070.005
Open science0.0030.006
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0030.000

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 head0.131
GPT teacher head0.492
Teacher spread0.360 · 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 designObservational
DomainMethods
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

Citations145
Published2008
Admission routes3
Has abstractyes

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