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Record W2022015103 · doi:10.1136/bmj.324.7344.1039/a

Proper benchmark for drug prescribing needs to be found

2002· letter· en· W2022015103 on OpenAlexaboutno aff
Uwe E. Reinhardt

Bibliographic record

VenueBMJ · 2002
Typeletter
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsIMGMinor (academic)Point (geometry)MedicineFamily medicineComputer scienceArtHumanitiesMathematics

Abstract

fetched live from OpenAlex

EDITOR—At the risk of annoying my Canadian friends who reported the influence of direct to consumer pharmaceutical advertising and patients' requests on prescribing,1 I would point out two studies published in JAMA . Firstly, Allison et al found that doctors at major teaching hospitals properly prescribed![Graphic][1] blockers (and other drugs) more frequently than did doctors at minor teaching hospitals and non-teaching hospitals.2 For ![Graphic][2] blockers, for example, major … [1]: /embed/inline-graphic-1.gif [2]: /embed/inline-graphic-2.gif

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)0.005
metaresearch head score (Gemma)0.067
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.041
Threshold uncertainty score0.138

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.067
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0050.004
Scholarly communication0.0060.009
Open science0.0030.002
Research integrity0.0400.037
Insufficient payload (model declined to judge)0.0410.037

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 head0.610
GPT teacher head0.554
Teacher spread0.056 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2002
Admission routes1
Has abstractyes

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