Validation of the International Classification of Functioning, Disability, and Health (ICF) Brief Core Set for osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: To validate the International Classification of Functioning, Disability, and Health (ICF) Brief Core Set for osteoarthritis (OA) by comparing the preliminary Brief Core Set to a selection of categories from the Comprehensive Core Set that explain most of the variance of functioning and health. METHODS: Patients with knee OA were asked to complete the Case Report Form for Patients, which includes the 36-item Short Form Health Survey (SF-36) and the Self-administered Comorbidity Questionnaire (SCQ). For each patient, the research staff was asked to complete the Case Report Form for Health Professionals, which includes the ICF Comprehensive Core Set for OA. Two individual questions regarding patients' general health and functioning were completed by both the patients and the research staff. The ICF categories to be entered into an initial regression model were selected following systematic steps in accordance with the ICF structure. Based on the initial models, additional models were generated by systematically substituting the ICF categories included in the initial models with other highly intercorrelated categories. RESULTS: A consecutive sample of 122 patients completed this study. Sixteen candidate ICF categories were identified by 15 linear regression models, which accounted for 5.5-57.7% of the total variance. Besides the two categories, b710 and b730, that are already included in the preliminary Brief Core Set, 14 additional categories were identified to be potential candidates for the Core Set. CONCLUSIONS: This study complemented the development of the Brief Core Set, which should be further refined by incorporating the opinions of patients, clinicians, and statisticians.
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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 | 0.044 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".