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Record W4387035137 · doi:10.1097/phm.0000000000002347

The Many Benefits of Reviewing for the American Journal of Physical Medicine and Rehabilitation

2023· editorial· en· W4387035137 on OpenAlexaffabout
Lawrence R. Robinson, Walter R. Frontera

Bibliographic record

VenueAmerican Journal of Physical Medicine & Rehabilitation · 2023
Typeeditorial
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsToronto Rehabilitation InstituteUniversity of Toronto
Fundersnot available
KeywordsRehabilitationMedicineSports medicineGerontologyFamily medicinePhysical therapy

Abstract

fetched live from OpenAlex

Physical Medicine and Rehabilitation, University of Toronto, Toronto, Ontario, Canada Department of Physical Medicine, Rehabilitation, and Sports Medicine/Department of Physiology, University of Puerto Rico School of Medicine, San Juan, Puerto Rico, USA Corresponding author: Larry Robinson, MD, St. John's Rehab, S125, 285 Cummer Ave., Toronto, ON M2M 2G1, Tel: 416-226-6780 ext 57274. Email: [email protected] Author Disclosure: Lawrence Robinson serves as an associate editor for the American Journal of Physical Medicine and Rehabilitation.Walter Frontera serves as editor-in-chief for the American Journal of Physical Medicine and Rehabilitation.

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)0.018
metaresearch head score (Gemma)0.091
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.982
Threshold uncertainty score0.109

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.091
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0050.003
Science and technology studies0.0050.003
Scholarly communication0.0120.005
Open science0.0030.001
Research integrity0.0140.015
Insufficient payload (model declined to judge)0.0330.031

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 head0.044
GPT teacher head0.457
Teacher spread0.413 · 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
DomainEvaluation
GenreEditorial

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

Citations0
Published2023
Admission routes2
Has abstractyes

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