A Systematic Review of Self-Reported Outcome Measures Assessing Disability Following Hand and Upper Extremity Conditions in Persian Population.
Bibliographic record
Abstract
BACKGROUND: Disability following hand and upper extremity conditions is common. Patient-reported outcome measures (PROs) are used to capture patients' status subjectively. This review has aimed to synthesis the literature regarding the extent and methodological quality of translation, cross-cultural adaptation, and psychometric properties of the hand and upper extremity disability PROs in the Persian language. METHODS: Seven electronic databases (MEDLINE, EMBASE, Psychinfo, Scopus, ISI, Science direct, and Google Scholar) were searched until May 2020. Studies reporting cross-cultural adaptation and psychometric properties testing of the Persian validated disability PROs of the hand and upper extremity were identified. We appraised the eligible studies using Guidelines for the Process of Cross-cultural Adaptation of Self-report Measures and COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) risk of bias checklist. RESULTS: Out of 98 identified records, 22 studies on 17 PROs were reviewed. Most of the PROs (47%) were region-specific and the others were condition-specific (29%) and multi-region (24%). Most of the studies (67%) followed 80 to 100% of the recommended steps for cross-cultural adaptation and translation of a PRO. The evidence of internal consistency, test-retest reliability, and construct validity was available for all the PROs. Structural validity, measurement error, and responsiveness were evaluated for five, six, and four PROs, respectively. The overall risk of bias ranged from "inadequate" to "very good" for all studies. CONCLUSION: A reasonable number of PROs for the evaluation of hand and upper extremity disability are available in the Persian language. Although all of them are not of very good psychometric properties, they all have sufficient quality to be used in clinical settings.
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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 | 0.012 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".