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Record W2078716028 · doi:10.1080/17453670710014284

How to interpret a meta-analysis and judge its value as a guide for clinical practice

2007· article· en· W2078716028 on OpenAlexaff
Michael Zlowodzki, Rudolf W. Poolman, Gino M. M. J. Kerkhoffs, Paul Tornetta, Mohit Bhandari, On behalf of the International Evid

Bibliographic record

VenueActa Orthopaedica · 2007
Typearticle
Languageen
FieldMedicine
TopicHip and Femur Fractures
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineValue (mathematics)Meta-analysisMEDLINEMedical physicsStatisticsInternal medicine

Abstract

fetched live from OpenAlex

In the era of evidence-based orthopedics, the number of meta-analyses has dramatically increased in the last decade (Bhandari et al. 2001). Meta-analyses statistically combine the results of multip...

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)0.410
metaresearch head score (Gemma)0.799
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: Methods · Consensus signal: Methods
Teacher disagreement score0.590
Threshold uncertainty score0.727

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4100.799
Meta-epidemiology (narrow)0.0070.005
Meta-epidemiology (broad)0.0260.018
Bibliometrics0.0250.012
Science and technology studies0.0050.012
Scholarly communication0.0180.012
Open science0.0130.005
Research integrity0.0170.025
Insufficient payload (model declined to judge)0.0110.006

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 head0.077
GPT teacher head0.436
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 designNot applicable
DomainMethods
GenreMethods

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

Citations120
Published2007
Admission routes1
Has abstractno

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