"Mc Gill Pain Questionnaire: A Cross-Cultural Adaptation Study in Chronic Neck Pain"
Bibliographic record
Abstract
Introduction: Due to its complex nature, identification, and treatment of both physical and psychological risk factors is essential in patients with neck pain. Multidimensional pain assessment is an essential prerequisite to planning a multi-modal treatment. McGill Pain Questionnaire is a valid and reliable tool that can assist in multidimensional pain assessment. Hence, this study's objective was to determine the clinimetric properties and usability of the Hindi version of the McGill Pain Questionnaire in patients with neck pain.Methods: After securing permission from the University Ethics board, a cross-culturally adapted Hindi version of the Long Form McGill Pain Questionnaire was administered to evaluate clinimetric properties (validity and reliability) in fifty patients with chronic neck pain.Results: Hindi version of Long Form McGill Pain Questionnaires demonstrated high levels of internal consistency (Cronbach alpha range 0.76- 0.83) and reliability (intraclass correlation coefficient range 0.74-0.85) in patients with chronic neck pain. The Hindi version of LF-MPQ demonstrated adequate construct and concurrent validity when tested with VAS (Pearson r- 0.80) and NDI (Pearson r- 0.79), respectively.Conclusion: The Hindi version of the LF-MPQ was a reproducible and valid tool in chronic neck pain assessment.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".