Validation of the French version of the KOOS-child questionnaire
Bibliographic record
Abstract
PURPOSE: The Knee Injury Osteoarthritis Outcome Score (KOOS) questionnaire is one of the frequently used outcome scores in pediatric studies. However, a recent study demonstrated that the pediatric population had a limited understanding of some of its questions. Therefore, the KOOS-Child questionnaire was developed specifically for this population. Our team produced a French adaptation based on the English version. The objective of the current study was to validate the French adaptation of the KOOS-Child questionnaire. METHODS: After ethic board approval, the questionnaire was translated from English to French by two French speaking orthopedic surgeons. Following consensus, the translated version was retranslated to English by a professional translator. A group of experts compared the original and back translated version and decided on a final adapted questionnaire version. Ninety-nine 8-16 year-old patients were prospectively recruited from our pediatric orthopedic surgery clinic. Twenty-one control participants and 78 patients suffering from knee pain were recruited. The participants were asked to answer the translated French version of the KOOS-Child questionnaire and two validated French pediatric quality of life surveys. RESULTS: Statistical analysis demonstrated no statistically significant demographic difference between the control population and the patients suffering from a knee pathology. The mean for the five different domains of the KOOS-Child questionnaire showed statistical differences (p < 0.001) between the two groups. Construct validity was demonstrated through testing of previously validated hypothesis of correlation. Internal consistency was also confirmed in injured patients. CONCLUSIONS: In conclusion, the current study results demonstrate good to excellent internal consistency, good construct validity and inconclusive discriminant capacity of the French adaptation of the KOOS-Child questionnaire. LEVEL OF EVIDENCE: II.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".