The Neck Disability Index–Russian Language Version (NDI-RU)
Bibliographic record
Abstract
STUDY DESIGN: Cross-cultural adaptation and psychometric testing. OBJECTIVE: To perform a validated Russian translation and then to evaluate the validity and reliability of the Russian language version of the Neck Disability Index (NDI-RU). SUMMARY OF BACKGROUND DATA: Neck pain is highly prevalent and can greatly affect daily activity. The Neck Disability Index (NDI) is the most frequently used scale for self-rating of disability due to neck pain. Its translated versions are applied in many countries. However, the Russian language version of the NDI has not been developed yet. METHODS: Cross-cultural adaptation of the NDI-RU was performed according to established guidelines. Then, the NDI-RU was evaluated for content validity, concurrent criterion validity, internal consistency, test-retest reliability, factor structure, and minimum detectable change. RESULTS: Two hundred thirty-two patients took part in the study in total: 109 in validity (39.5 ± 10 yr), 123 in reliability (38.4 ± 11 yr; 80 in the test-retest phase). A culturally valid translation was achieved. NDI-RU total scores were distributed normally. Floor/ceiling effects were absent. Good values of Cronbach α were obtained for each item (from 0.80 to 0.84) and for the total NDI-RU (0.83). A 2-factor solution was found for the NDI-RU. The average interitem correlation coefficient was 0.53. Intraclass correlation coefficients for test-retest reliability coefficients ranged from 0.65 to 0.92 for different items and 0.91 for the total NDI-RU. Moderate correlation (Spearman rs = 0.62; P < 0.05) was found between the NDI-RU total score and graphic rating scalepain score. Completion of the NDI-RU takes 3.6 ± 1 minutes. CONCLUSION: The development of a Russian language version of the Neck Disability Index resulted in a valid, reliable instrument that can be used both in clinical practice and scientific investigations. LEVEL OF EVIDENCE: 1.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".