Development and Validation of the Chinese Version of the Massachusetts General Hospital Acupuncture Sensation Scale: An Exploratory and Methodological Study
Bibliographic record
Abstract
BACKGROUND: The Massachusetts General Hospital Acupuncture Sensation Scale (MASS) is a tool to measure needle sensations. The aims of the present study were to develop a Chinese version and to assess its psychometric properties. METHODS: This study was a methodological and exploratory study. The English version of the MASS was translated into Chinese using standardised translation procedures. Content validity was conducted by nine acupuncture experts. The prefinal Chinese version (C-MASS) was then administered to 30 acupuncture-naïve, healthy subjects. Electroacupuncture was performed on the right LI4 and LI11 acupoints for 30 min. A test-retest reliability measurement was administered 1-2 weeks later. Construct validity was examined by comparing results from C-MASS and the Short-Form McGill Pain Questionnaire (SF-MPQ). The construct validity was further assessed by the principle component analysis. RESULTS: C-MASS demonstrated a content validity ratio on relevance and importance from -0.04 to 1.00. Convergent validity was demonstrated by its significant association with the sensory dimension of SF-MPQ (γ=0.63, p<0.05). Discriminant validity was demonstrated by its low association with the affective dimension of SF-MPQ (γ=-0.3, p=0.111). A five-factor structure of C-MASS was established by factor analysis. C-MASS demonstrated good internal consistency (Cronbach's α=0.71) and test-retest reliability (intraclass correlation coefficient=0.92). Since the descriptor 'sharp pain' was not a valid needle sensation related to deqi, this was removed from C-MASS. We renamed the scale as the Modified MASS-Chinese version (C-MMASS). CONCLUSIONS: A 12-descriptor C-MMASS was established and shown to be a reliable and valid tool in reporting needle sensations associated with deqi among healthy young Chinese people.
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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 | 0.035 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".