Bibliographic record
Abstract
We read the recent Clinimetrics summary of the Upper Extremity Functional Index, which is a patient-reported outcome measure of upper extremity function in people with upper extremity disorders.1 We appreciate the authors' description of this index, as well as its validity, reliability and cross-cultural adaptations into other languages.The authors stated that this questionnaire has been cross-culturally adapted into various languages such as Turkish, 2 French Canadian 3 and Spanish. 4 However, the Upper Extremity Functional Index has been cross-culturally adapted into Turkish only. 2 The other translations that the authors noted are related to the Upper Limb Functional Index.Various region-specific outcome measures are available to assess upper extremity function, such as the Upper Extremity Functional Index, Upper Limb Functional Index and Upper Extremity Functional Scale.The Upper Extremity Functional Index, which was developed in 2001, 5 was translated into Turkish in 2015. 2 It is a 20-item outcome measure in which the patient gives each of a series of functional activities a score of 0 (extreme difficulty) to 4 (no difficulty).The scores of all 20 items are summed to give a total score ranging from 0 to 80.The highest possible total score of 80 means that the patient has no difficulty in completing any of the functional activities.5 The Upper Limb Functional Index, which was developed in 2006, 6 is another patient-reported measure that has been translated into multiple languages: Turkish, 7 French Canadian, 3 Spanish, 4 Italian 8 and Korean.9 The Upper Limb Functional Index has 25 items and each item is rated on an ordinal scale of 'yes', 'partly' or 'no' by the patient.These items are scored by assigning 1 point for 'yes', 0.5 points for 'partly' and 0 points for 'no'.The final score ranges from 0 to 100%, calculated by adding all the points, multiplying by 4 and then subtracting from 100.The highest score of 100% means that the patient has no functional limitation or disability.
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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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.065 | 0.033 |
| Insufficient payload (model declined to judge) | 0.021 | 0.016 |
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".