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Record W2997333031 · doi:10.1016/j.apmr.2019.11.011

Psychometric Properties of Performance-Based Functional Tests in Patients With Shoulder Pathologies: A Systematic Review and Meta-analysis

2019· review· en· W2997333031 on OpenAlexafffund
Goris Nazari, Steve Lu, Joy C. MacDermid, Aziza Azizi, Nicole Stokes, Samantha Hiller, Alex Kim, Kiarash Akhavan

Bibliographic record

VenueArchives of Physical Medicine and Rehabilitation · 2019
Typereview
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsSt. Joseph's HospitalMcMaster UniversityWestern University
FundersCanadian Institutes of Health ResearchWestern UniversityHealth Research
KeywordsMedicinePhysical therapyIntraclass correlationInter-rater reliabilityPhysical medicine and rehabilitationRehabilitationMeta-analysisReliability (semiconductor)Quality of life (healthcare)Construct validityCriterion validityPsychometricsPsychologyClinical psychologyRating scalePathology

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)0.017
metaresearch head score (Gemma)0.045
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.018
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.045
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0180.037
Bibliometrics0.0040.006
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.092
GPT teacher head0.351
Teacher spread0.259 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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

Citations15
Published2019
Admission routes2
Has abstractno

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