Crosscultural Adaptation, Reliability, and Validity of the Japanese Version of the Neck Disability Index
Bibliographic record
Abstract
STUDY DESIGN: Translation and psychometric testing. OBJECTIVE: To translate and culturally adapt the Neck Disability Index (NDI) and to assess the reliability and validity of the Japanese version of the NDI (NDI-J) in Japanese outpatients with neck pain. SUMMARY OF BACKGROUND DATA: To date, no previous report exists on the translation process and psychometric testing of the NDI-J. METHODS: The NDI was translated and culturally adapted into Japanese in accordance with published guidelines. A total of 110 outpatients with neck pain participated in the study. Psychometric testing included reliability by internal consistency (Cronbach α) and test-retest reliability (intraclass correlation coefficient), factor analysis, convergent validity by comparing the NDI-J with the short-form health survey (Pearson correlation) and responsiveness (unpaired t tests, standard error of measurement, and minimal detectable change). RESULTS: The Cronbach α of the NDI-J was 0.88 and the intraclass correlation coefficient for test-retest reliability was 0.91 (95% confidence interval, 0.82-0.95). Factor analysis demonstrated a 2-factor structure, explaining 61.8% of the total variance. The correlation between the NDI-J and the short-form health survey, version 36, subscales ranged from good to fair (-0.25 to -0.51). The analysis of responsiveness was calculated with an unpaired t test after 3 weeks of treatment demonstrating a statistically significant difference between the stable and improved patients (P ≤ 0.05). The standard error of measurement and minimal detectable change were calculated as 2.9 and 6.8, respectively. CONCLUSION: The NDI-J is a valid, reliable, and responsive tool that can be used to assess neck pain in Japanese outpatients.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".