MULTI-CENTERED LINGUISTIC ADAPTATION AND VALIDATION OF SHORT-FORM MCGILL PAIN QUESTIONNAIRE-2
Bibliographic record
Abstract
Objective: To achieve a linguistic adaptation and validation of Chinese version of Short-form McGill Pain Questionnaire-2(SF-MPQ-2).Methods: Two forward translations were made followed by comparison and reconciliation of the translations.Comparison of the two backward translations with the original version was made for further validation.Pilot testing and pain specialist' evaluations were also required.A total of 140 patients were enrolled in ten centers throughout China,70 neuropathic pain patients and 70 nociceptive pain patients respectively.Reliability(Cronbach's α coefficients and Guttman split-half coefficients) and validity(face validity and structure validity including factor analysis and Spearman correlation coefficients) of SF-MPQ-2 were determined.Results: Chinese version of SF-MPQ-2 had a good reliability(Cronbach's alpha coefficients and Guttman split-half coefficients were greater than 0.7).Validity of the Chinese version of SF-MPQ-2 was high.Four common factors were extracted.The contribution rate of accumulative total of variance of the four factors was 52.631%.The dimensional factor loading of each item was greater than 0.40.The spearman's rank correlation coefficients of the items were greater than 0.40 except for one item.Conclusion: The Chinese versions of SF-MPQ-2 developed and validated by this study can be used to evaluate both neuropathic pain and non-neuropathic pain in patients whose native language is Chinese(Mandarin).
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".