The Brazilian version of the Constant–Murley Score (CMS-BR): convergent and construct validity, internal consistency, and unidimensionality
Bibliographic record
Abstract
Objectives To translate and culturally adapt the CMS and assess the validity of the Brazilian version (CMS-BR). Methods The translation was carried out according to the back-translation method by four independent translators. The produced versions were synthesized through extensive analysis and by consensus of an expert committee, reaching a final version used for the cultural adaptation. A field test was conducted with 30 subjects in order to obtain semantic considerations. For the psychometric analyzes, the sample was increased to 110 participants who answered two instruments: CMS-BR and the Disabilities of the Arm, shoulder and Hand (DASH). The CMS-BR and DASH score range from 0 to 100 points. For the first, higher points reflect better function and for the latter, the inverse is true. The validity was verified by Pearson's correlation test, the unidimensionality by factorial analysis, and the internal consistency by Cronbach's alpha. Results The explained variance was 60.28% with factor loadings ranging from 0.60 to 0.91. The CMS-BR exhibited strong negative correlation with the DASH score (−0.82, p < 0.05), Cronbach's alpha 0.85, and its total score was strongly correlated with the patient's range of motion (0.93, p < 0.001). Conclusion The CMS was satisfactorily adapted for Brazilian Portuguese and demonstrated evidence of validity that allows its use in this population.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".