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Record W3202661697 · doi:10.1016/j.joca.2021.08.011

Development of an intervention to manage knee osteoarthritis risk and symptoms following anterior cruciate ligament injury

2021· article· en· W3202661697 on OpenAlexafffund
Aileen M. Davis, R. Wong, K. Steinhart, L. Cruz, David Cudmore, Tim Dwyer, Linda Li, P. Marks, Rhona McGlasson, Nathan Urquhart, Janie L. Astephen Wilson, Laura Nimmon, Darrell Ogilvie‐Harris, Jaskarndip Chahal

Bibliographic record

VenueOsteoarthritis and Cartilage · 2021
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsMcMaster UniversityDartmouth General HospitalUniversity Health NetworkHealth Sciences CentreBone and Joint CanadaSunnybrook Health Science CentreUniversity of British ColumbiaMount Sinai HospitalUniversity of TorontoDalhousie UniversityResearch CanadaSt. Francis Xavier UniversityWomen's College Hospital
FundersCanadian Institutes of Health Research
KeywordsAnterior cruciate ligamentOsteoarthritisMedicineIntervention (counseling)ACL injuryPhysical therapyInfluencer marketingPhysical medicine and rehabilitationNursingAlternative medicineSurgery

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.005
GPT teacher head0.248
Teacher spread0.243 · 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 designNon-randomized trial
Domainnot available
GenreEmpirical

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

Citations8
Published2021
Admission routes2
Has abstractno

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