A Systematic Review of Cross-Cultural Adaptation of the Neck Disability Index
Bibliographic record
Abstract
In Brief Study Design. Systematic review of cross-cultural adaptation. Objective. To perform a systematic review of cross-cultural adaptations of the Neck Disability Index (NDI) and to give a critical assessment to improve its translation. Summary of Background Data. The NDI is used to assess functional capacity and physical activity in patients with neck pain, but the quality of its cross-cultural adaptations has not been systematically reviewed. Methods. PubMed, Cochrane Library, and EMBASE were searched up through 2013 to identify studies of cross-cultural NDI adaptations. Search terms were “Neck Disability Index” or “NDI” and “cross-cultur*” or “cultur*” or “valid*” or “equivalence” or “transl*.” Data were extracted and study quality was assessed. Results. Twenty-four different NDI versions were identified from 14 different languages/cultures. Most reported forward and back translation and pretesting, but sample size was a problem for most studies. The Cronbach α was generally acceptable, and 13 versions met the criterion of reliability by reporting an intraclass correlation coefficient of 0.70 or more, although some versions did not reach the minimal intraclass correlation coefficient. Eleven versions tested ceiling and floor effects, but only 1 Japanese version reported a floor effect. No study reported interpretability, and none provided the minimal important change or minimal important difference. Conclusion. The Arabic, Italian, and Thai versions were of higher quality than the other versions according to the overall assessment of the 3 checklists. The Catalan, Chinese, Japanese, Korean, Thai, and Turkish versions need more research according to the Quality Criteria for Psychometric Properties of Health Status Questionnaire. Pretest sample size was not large enough in most cases. Level of Evidence: 1 The aim of our article is to perform a systematic review of available cross-cultural adaptations of the Neck Disability Index and to give a critical assessment to improve its translation. The Arabic, Italian, and Thai versions had higher quality than others according to the overall assessment of the 3 checklists. Catalan, Chinese, Japanese, Korean, Thai, and Turkish need more research according to the Quality Criteria for Psychometric Properties of Health Status Questionnaire. Pretest sample size was not large enough in most cases.
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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 | 0.021 | 0.088 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".