Paper 75: Development and Validation of the KOOS-ACL: A Short-form Version of the KOOS for Young Patients with ACL Tears
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".