Translation of Oswestry Disability Index into Tamil with Cross Cultural Adaptation and Evaluation of Reliability and Validity§
Bibliographic record
Abstract
STUDY DESIGN: Prospective longitudinal validation study. OBJECTIVE: To translate and cross-culturally adapt the Oswestry Disability Index (ODI) to the Tamil language (ODI-T), and to evaluate its reliability and construct validity. SUMMARY OF BACKGROUND DATA: ODI is widely used as a disease specific questionnaire in back pain patients to evaluate pain and disability. A thorough literature search revealed that the Tamil version of the ODI has not been previously published. METHODS: The ODI was translated and cross-culturally adapted to the Tamil language according to established guidelines. 30 subjects (16 women and 14 men) with a mean age of 42.7 years (S.D. 13.6; Range 22 - 69) with low back pain were recruited to assess the psychometric properties of the ODI-T Questionnaire. Patients completed the ODI-T, Roland-Morris disability questionnaire (RMDQ), VAS-pain and VAS-disability at baseline and 24-72 hours from the baseline visit. RESULTS: The ODI-T displayed a high degree of internal consistency, with a Cronbach's alpha of 0.92. The test-retest reliability was high (n=30) with an ICC of 0.92 (95% CI, 0.84 to 0.96) and a mean re-test difference of 2.6 points lower on re-test. The ODI-T scores exhibited a strong correlation with the RMDQ scores (r = 0.82) p<0.01, VAS-P (r = 0.78) p<0.01 and VAS-D (r = 0.81) p<0.01. Moderate to low correlations were observed between the ODI-T and lumbar ROM (r = -0.27 to -0.53). All the hypotheses that were constructed apriori were supported. CONCLUSION: The Tamil version of the ODI Questionnaire is a valid and reliable tool that can be used to measure subjective outcomes of pain and disability in Tamil speaking patients with low back pain.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".