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Record W2963371006 · doi:10.1136/bmj.l4606

Distinguishing opinion from evidence in guidelines

2019· article· en· W2963371006 on OpenAlexaff
Holger J. Schünemann, Yuqing Zhang, Andrew D Oxman

Bibliographic record

VenueBMJ · 2019
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsMcMaster UniversityImpactHealth Sciences CentreMcMaster University Medical Centre
Fundersnot available
KeywordsExpert opinionSecond opinionPsychologyPolitical scienceMedicinePathologyIntensive care medicine

Abstract

fetched live from OpenAlex

The experience of experts can be useful when developing guidelines, but structures need to be in place to avoid opinion being confused with evidence, say Holger J Schünemann and colleagues

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)0.368
metaresearch head score (Gemma)0.798
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.632
Threshold uncertainty score0.780

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3680.798
Meta-epidemiology (narrow)0.0010.004
Meta-epidemiology (broad)0.0060.005
Bibliometrics0.0150.008
Science and technology studies0.0110.051
Scholarly communication0.0420.056
Open science0.0080.031
Research integrity0.0370.063
Insufficient payload (model declined to judge)0.0070.004

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 head0.579
GPT teacher head0.592
Teacher spread0.013 · 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 designTheoretical or conceptual
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

Citations109
Published2019
Admission routes1
Has abstractyes

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