Adapted version of the mcgill pain questionnaire to Brazilian Portuguese
Bibliographic record
Abstract
The purpose of this study was to the present a translated version of the McGill Pain Questionnaire to Brazilian Portuguese that adapted the original pain descriptors according to the Brazilian culture, aiming at its scientific validation. Initially, the original questionnaire was translated by 3 legally recognized translators fluent in English and in Brazilian Portuguese. The translations were meticulously assessed by 5 health professionals (3 dentists, 1 physician and 1 medical student) who were asked to choose the best translation for each pain descriptor of the original questionnaire in English. The resulting questionnaire was applied to 80 subjects (20 professors, 20 dental students, 20 employees and 20 patients, all related to the School of Dentistry of Ribeirão Preto, University of São Paulo). After some adjustments to improve the understanding of the pain descriptors, an adaptation of the intensity values of each pain descriptor was done by 20 postgraduate dental students and 20 undergraduate dental students, who were asked to record, for each word, the pain intensity value based on their personal opinion. In addition, they were asked to fill out the final version of the questionnaire to identify any doubts. The McGill Pain Questionnaire proved to be a very useful tool for measuring pain, and its version in Brazilian Portuguese was validated to be used as an important diagnostic resource.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".