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Record W4362692287 · doi:10.1177/03635465231160726

External Validation of the KOOS-ACL in the MOON Group Cohort of Young Athletes Followed for 10 Postoperative Years

2023· article· en· W4362692287 on OpenAlexafffundabout
Hana Marmura, Paul F. Tremblay, Dianne Bryant, Kurt P. Spindler, Laura J. Huston, Alan Getgood

Bibliographic record

VenueThe American Journal of Sports Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsImpactFowler Kennedy Sport Medicine ClinicMcMaster UniversityLondon Health Sciences CentreLawson Health Research InstituteWestern University
FundersCanadian Institutes of Health ResearchGraymontNational Institutes of HealthNational Institute of Arthritis and Musculoskeletal and Skin DiseasesInternational Society of Arthroscopy, Knee Surgery and Orthopaedic Sports MedicineU.S. Department of Defense
KeywordsMedicineAnterior cruciate ligamentPhysical therapyCohortOsteoarthritisAthletesConvergent validityCohort studySurgeryInternal consistencyPatient satisfactionInternal medicine

Abstract

fetched live from OpenAlex

Background: The Knee injury and Osteoarthritis Outcome Score–Anterior Cruciate Ligament (KOOS-ACL) is a short form version of the KOOS, developed to target populations of young active patients with ACL tears. The KOOS-ACL consists of 2 subscales: Function (8 items) and Sport (4 items). The KOOS-ACL was developed and validated using data from the Stability 1 study from baseline to postoperative 2 years. Purpose: To validate the KOOS-ACL in an external sample of patients matching the outcome’s target population. Study Design: Cohort study (diagnosis); Level of evidence, 1. Methods: The Multicenter Orthopaedic Outcomes Network group cohort of 839 patients aged 14 to 22 years who tore their ACLs while playing sports was used to assess internal consistency reliability, structural validity, convergent validity, responsiveness to change, and floor/ceiling effects of the KOOS-ACL at 4 time points: baseline and postoperative 2, 6, and 10 years. Detection of treatment effects between graft type (hamstring tendon vs bone–patellar tendon–bone) were also compared between the full-length KOOS and KOOS-ACL. Results: The KOOS-ACL demonstrated acceptable internal consistency reliability (α = .82-.89), structural validity (Tucker-Lewis index and comparative fit index = 0.98-0.99; standardized root mean square residual and root mean square error of approximation = 0.04-0.07), convergent validity (Spearman correlation with International Knee Documentation Committee subjective knee form = 0.66-0.85; Western Ontario and McMaster Universities Osteoarthritis Index function = 0.84-0.95), and responsiveness to change across time (large effect sizes from baseline to postoperative 2 years; d = 0.94 [Function] and d = 1.54 [Sport]). Stable scores and significant ceiling effects were seen from 2 to 10 years. No significant differences in KOOS or KOOS-ACL scores were detected between patients with different graft types. Conclusion: The KOOS-ACL shows improved structural validity when compared with the full-length KOOS and adequate psychometric properties in a large external sample of high school and college athletes. This strengthens the argument to use the KOOS-ACL to assess young active patients with ACL tears in clinical research and practice.

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)0.009
metaresearch head score (Gemma)0.016
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 score0.009
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.011
GPT teacher head0.289
Teacher spread0.278 · 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

Citations8
Published2023
Admission routes3
Has abstractyes

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