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Record W4379768840 · doi:10.2519/jospt.2023.11838

Knee- and Overall Health-Related Quality of Life Following Anterior Cruciate Ligament Injury: A Cross-sectional Analysis of Australian and Canadian Cohorts

2023· article· en· W4379768840 on OpenAlexaffabout
Brooke Patterson, Carolyn Emery, Kay M. Crossley, Adam G Culvenor, Jean‐Michel Galarneau, Jacob L. Jaremko, Clodagh Toomey, Ali Guermazi, Jackie L. Whittaker

Bibliographic record

VenueJournal of Orthopaedic and Sports Physical Therapy · 2023
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsResearch CanadaUniversity of British ColumbiaUniversity of AlbertaAlberta Bone and Joint Health InstituteArthritis Research Centre of CanadaUniversity of Calgary
Fundersnot available
KeywordsMedicineAnterior cruciate ligamentPhysical therapyQuality of life (healthcare)OsteoarthritisKnee painCross-sectional studyConfidence intervalACL injuryCohort studyKnee JointInternal medicineSurgery

Abstract

fetched live from OpenAlex

OBJECTIVE: To describe the knee- and overall health-related quality of life (QOL) 3 to 12 years after anterior cruciate ligament (ACL) tear, and to assess the association of clinical and structural features with QOL after ACL tear. DESIGN: Cross-sectional analysis of combined data from Australian (n = 76, 5.4 years postinjury) and Canadian (n = 50, 6.6 years postinjury) prospective cohort studies. METHODS: We conducted a secondary analysis of patient-reported outcomes and index knee magnetic resonance imaging (MRI) acquired in 126 patients (median 5.5 [range: 4–12] years postinjury), all treated with ACL reconstruction. Outcomes included knee (ACL Quality of Life questionnaire [ACL-QOL]) and overall health-related QOL (EQ-5D-3L). Explanatory variables were self-reported knee pain (Knee Injury and Osteoarthritis Outcome Score [KOOS-Pain subscale]) and function (KOOS-Sport subscale), and any knee cartilage lesion (MRI Osteoarthritis Knee Score). Generalized linear models were adjusted for clustering between sites. Covariates were age, sex, time since injury, injury type, subsequent knee injuries, and body mass index. RESULTS: The median [range] ACL-QOL score was 82 [24–100] and EQ-5D-3L was 1.0 [−0.2 to 1.0]. For every 10-point higher KOOS-Sport score, the ACL-QOL score increased by 3.7 points (95% confidence interval [CI]: 1.7, 5.7), whereas there was no evidence of an association with the EQ-5D-3L (0.00 points, 95% CI: −0.02, 0.02). There were no significant association between KOOS-Pain and ACL-QOL (4.9 points, 95% CI: −0.1, 9.9) or EQ-5D-3L (0.05 points, 95% CI: −0.01, 0.11), respectively. Cartilage lesions were not associated with ACL-QOL (−1.2, 95% CI: −5.1, 2.7) or EQ-5D-3L (0.01, 95% CI: −0.01, 0.04). CONCLUSION: Self-reported function was more relevant for knee-related QOL than knee pain or cartilage lesions after ACL tear. Self-reported function, pain, and knee structural changes were not associated with overall health-related QOL. J Orthop Sports Phys Ther 2023;53(7):402–413. Epub: 8 June 2023. doi:10.2519/jospt.2023.11838

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score0.223

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.347
Teacher spread0.320 · 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 designObservational
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

Citations4
Published2023
Admission routes2
Has abstractyes

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