Comparison of generic patient-reported outcome measures used with upper extremity musculoskeletal disorders: Linking process using the International Classification of Functioning, Disability, and Health (ICF)
Bibliographic record
Abstract
OBJECTIVE: To report the theoretical foundation of generic patient-reported outcomes for measuring functioning related to upper extremity musculoskeletal disorders and perform content coverage analysis and content comparison using the International Classification of Functioning, Disability and Health (ICF). METHODS: A literature search was performed to identify commonly used patient-reported outcomes. A comparison of their theoretical foundations and a linking exercise between the measures' meaningful concepts and the ICF and Brief ICF Core Set for Hand Conditions was accomplished based on established rules. RESULTS: Fifteen measures were selected. Multiple theoretical foundations were identified, and only 7 measures were developed based on a known conceptual model. Six measures were chosen for the linking process with 232 meaningful concepts retrieved and linked to 54 ICF categories. No concept was linked to the Body Structures component and two measures stood out for their Activity and Participation coverage. No measure covered all Brief ICF Core Set for Hand Conditions recommended categories. CONCLUSION: Some heterogeneity was observed with regards to the theoretical foundations on which the identified measures are based. The results of the linking process should help reduce these inconsistencies. They enable easy identification of content coverage and content comparison between measures using a common framework and can be used as a reference when selecting the most appropriate patient-reported outcome measure.
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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 | 0.048 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.021 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".