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Record W2893827325 · doi:10.1016/j.apmr.2018.09.112

The Rehabilitation Treatment Specification System: Implications for Improvements in Research Design, Reporting, Replication, and Synthesis

2018· article· en· W2893827325 on OpenAlexaff
Jarrad H. Van Stan, Marcel Dijkers, John Whyte, Tessa Hart, Lyn S. Turkstra, Jeanne M. Zanca, Christine Chen

Bibliographic record

VenueArchives of Physical Medicine and Rehabilitation · 2018
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsMcMaster University
FundersOffice of Behavioral and Social Sciences ResearchNational Institutes of HealthNational Institute on Deafness and Other Communication DisordersPatient-Centered Outcomes Research InstituteMassachusetts General Hospital
KeywordsReplication (statistics)RehabilitationPhysical medicine and rehabilitationPsychologyPhysical therapyMedicine

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.845
metaresearch head score (Gemma)0.940
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: Methods · Consensus signal: Methods
Teacher disagreement score0.155
Threshold uncertainty score0.210

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.8450.940
Meta-epidemiology (narrow)0.0040.006
Meta-epidemiology (broad)0.0090.018
Bibliometrics0.0140.020
Science and technology studies0.0090.011
Scholarly communication0.0200.020
Open science0.0110.016
Research integrity0.0080.012
Insufficient payload (model declined to judge)0.0140.003

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 head0.182
GPT teacher head0.527
Teacher spread0.346 · 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
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

Citations185
Published2018
Admission routes1
Has abstractno

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