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Record W1144648383 · doi:10.1016/j.math.2015.08.011

The effectiveness of soft-tissue therapy for the management of musculoskeletal disorders and injuries of the upper and lower extremities: A systematic review by the Ontario Protocol for Traffic Injury management (OPTIMa) collaboration

2015· review· en· W1144648383 on OpenAlexaffabout
Steven Piper, Heather M. Shearer, Pierre Côté, Jessica J. Wong, Hainan Yu, Sharanya Varatharajan, Danielle Southerst, Kristi Randhawa, Deborah Sutton, Maja Stupar, Margareta Nordin, Silvano Mior, Gabrielle M. van der Velde, Anne Taylor‐Vaisey

Bibliographic record

VenueManual Therapy · 2015
Typereview
Languageen
FieldMedicine
TopicTendon Structure and Treatment
Canadian institutionsInstitute for Work & HealthCentre for Disability Prevention and RehabilitationOntario Tech UniversityUniversity of TorontoCanadian Memorial Chiropractic College
FundersMars
KeywordsMedicineEpicondylitisPlantar fasciitisMassagePlantar fasciaPhysical therapyMyofascial releaseSoft tissueManual therapyCarpal tunnel syndromePhysical medicine and rehabilitationMyofascial pain syndromeTennis elbowSurgeryHeelAlternative medicine

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)0.010
metaresearch head score (Gemma)0.033
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 score0.038
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.033
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0120.013
Bibliometrics0.0080.007
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.020
GPT teacher head0.371
Teacher spread0.351 · 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

Citations49
Published2015
Admission routes2
Has abstractno

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