MétaCan
Menu
Back to cohort
Record W2021390933 · doi:10.1136/bmj.39185.394005.ad

Should patient groups accept money from drug companies? No

2007· letter· en· W2021390933 on OpenAlexfundno aff
Barbara Mintzes

Bibliographic record

VenueBMJ · 2007
Typeletter
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsnot available
FundersMinistry of Health, British Columbia
KeywordsIndependence (probability theory)Work (physics)Limit (mathematics)Drug industryBusinessLaw and economicsPublic relationsEconomicsPolitical scienceEngineering ethicsEngineering

Abstract

fetched live from OpenAlex

Patient groups provide valuable support and advocacy for vulnerable people but funding the work can be difficult. Alastair Kent argues that not accepting industry money will unnecessarily limit the groups9 effectiveness, but Barbara Mintzes believes that the money undermines their independence

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)0.015
metaresearch head score (Gemma)0.061
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.878
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.061
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0090.010
Scholarly communication0.0070.017
Open science0.0040.005
Research integrity0.1220.070
Insufficient payload (model declined to judge)0.0240.015

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 head0.483
GPT teacher head0.549
Teacher spread0.066 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
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

Citations58
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueBMJSame topicPharmaceutical industry and healthcareFrench-language works237,207