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Record W2011510931 · doi:10.1016/j.math.2015.03.012

The effectiveness of exercise on recovery and clinical outcomes of soft tissue injuries of the leg, ankle, and foot: A systematic review by the Ontario Protocol for Traffic Injury Management (OPTIMa) Collaboration

2015· review· en· W2011510931 on OpenAlexaffabout
Erin Woitzik, Craig Jacobs, Jessica J. Wong, Pierre Côté, Heather M. Shearer, Kristi Randhawa, Deborah Sutton, Danielle Southerst, Sharanya Varatharajan, Robert J. Brison, Hainan Yu, Gabrielle van der Velde, Paula Stern, Anne Taylor‐Vaisey, Maja Stupar, Silvano Mior, Linda Carroll

Bibliographic record

VenueManual Therapy · 2015
Typereview
Languageen
FieldMedicine
TopicTendon Structure and Treatment
Canadian institutionsUniversity of AlbertaUniversity of TorontoQueen's UniversityInstitute for Work & HealthKingston General HospitalCentre for Disability Prevention and RehabilitationHotel Dieu HospitalUniversity of Ontario Institute of TechnologyCanadian Memorial Chiropractic College
FundersFinancial Services Commission
KeywordsMedicinePlantar fasciitisPlantar fasciaHeelPhysical therapyAnklePhysical medicine and rehabilitationTendinopathyRehabilitationRandomized controlled trialPsychological interventionSurgeryTendon

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)0.008
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.031
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.029
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0130.015
Bibliometrics0.0060.006
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.045
GPT teacher head0.428
Teacher spread0.384 · 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 designSystematic review
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

Citations16
Published2015
Admission routes2
Has abstractno

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