Adaptação ao português do Brasil da ferramenta de classificação da obesidade Edmonton Obesity Staging System
Bibliographic record
Abstract
Objective: Cross-cultural adaptation into Brazilian Portuguese of the five-stage Edmonton Obesity Staging System (EOSS), which classifies the severity of obesity according to morbidities and health risks. Methods: The following steps were taken: (1) translation and synthesis; (2) semantic analysis by a committee of subject experts and linguists; (3) construction of complementary content and validation by a panel of experts, in two rounds; (4) back-translation and submission to the original author; (5) semantic evaluation by health professionals; and (6) pre-testing on a sample of people with obesity. Semantic analysis and complementary content validation were verified by 80% minimum concordance. Results: The tool maintained semantic, idiomatic, conceptual, and cultural equivalence with the original version. The semantic evaluation showed adequate understanding by the target audience, with over 80% concordance. Conclusion: The version of the EOSS adapted for Brazil has proved to be a robust and useful tool for assessing obesity in the Brazilian context. It meets rigorous methodological standards, indicating its use in research and clinical practice as an important tool for assessing people with obesity.
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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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".