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Record W2054647247 · doi:10.1080/17453670710013636

Don't be misled by the orthopedic literature : Tips for critical appraisal

2007· article· en· W2054647247 on OpenAlexaff
Rudolf W. Poolman, Gino M. M. J. Kerkhoffs, Peter Struijs, Mohit Bhandari, On behalf of the International Evid

Bibliographic record

VenueActa Orthopaedica · 2007
Typearticle
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineOrthopedic surgeryCritical appraisalMedical literatureMEDLINESurgeryGeneral surgeryAlternative medicineLawPathology

Abstract

fetched live from OpenAlex

Surgeons are constantly inundated with medical information. There are over 4,000 different journals in PubMed and over 10,000 new citations are added to the medical literature every week. Unfortuna...

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)0.197
metaresearch head score (Gemma)0.615
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.803
Threshold uncertainty score0.990

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1970.615
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0060.003
Bibliometrics0.0130.006
Science and technology studies0.0080.018
Scholarly communication0.0150.026
Open science0.0080.006
Research integrity0.0180.028
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 head0.076
GPT teacher head0.497
Teacher spread0.421 · 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

Citations33
Published2007
Admission routes1
Has abstractyes

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