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Record W4288456444 · doi:10.1177/2325967121s00638

Paper 75: Development and Validation of the KOOS-ACL: A Short-form Version of the KOOS for Young Patients with ACL Tears

2022· article· en· W4288456444 on OpenAlexaff
Paul R. Tremblay, Alan Getgood, Dianne Bryant, Hana Marmura

Bibliographic record

VenueOrthopaedic Journal of Sports Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsFowler Kennedy Sport Medicine ClinicWestern University
Fundersnot available
KeywordsMedicineConfirmatory factor analysisDiscriminant validityPopulationExploratory factor analysisStructural equation modelingPhysical therapyClinical psychologyInternal consistencyPsychometricsStatisticsMathematics

Abstract

fetched live from OpenAlex

Objectives: To develop and validate a short form, disease-specific version of the KOOS appropriate for the young active ACL deficient population: the KOOS-ACL. Methods: A baseline dataset of 605 young patients (< 25 years) with ACL tears was divided into a development and validation sample. Exploratory factor analyses were conducted in the development sample to identify the underlying factor structure and reduce the number of KOOS items based on statistical and conceptual indicators. Confirmatory factor analyses were conducted to check fit indices of the proposed KOOS-ACL model in both samples. Structural validity, reliability, and responsiveness to change were assessed in the full sample at five timepoints: baseline and 3 months, 6 months, 12 months and 24 months post-operatively. Results: Two factors were deemed most appropriate for the KOOS-ACL: Functionality and Sport. Fifteen items were removed from the full length KOOS based on discriminant validity (i.e., lack of distinctiveness between some proposed constructs) and another fifteen items were removed for repetitive content. The final KOOS-ACL model showed acceptable structural validity (CFI and TLI > 0.9, RMSEA and SRMR < 0.08), internal consistency reliability (a > 0.8), and responsiveness to change (effect size > 0.8) at all five timepoints in the dataset. The KOOS-ACL showed strong and significant correlations to the original KOOS and IKDC at all timepoints (r > 0.7). Conclusions: The new KOOS-ACL questionnaire contains 12 items and two subscales relevant to young active ACL patients. The KOOS-ACL would reduce patient burden by more than two thirds and provides improved structural validity compared to the full length KOOS while maintaining adequate psychometric properties and relatedness to other popular outcome measures currently used following ACL injuries. The KOOS-ACL may be a more relevant outcome to use with young active ACL patients within the two years of surgery.

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)0.024
metaresearch head score (Gemma)0.050
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: Methods · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.050
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.003

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 head0.008
GPT teacher head0.231
Teacher spread0.223 · 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
GenreMethods

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

Citations1
Published2022
Admission routes1
Has abstractyes

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