Tradução e adaptação cultural à língua portuguesa do American Shoulder and Elbow Surgeons Standardized Shoulder Assessment Form (ASES) para avaliação da função do ombro
Bibliographic record
Abstract
Shoulder pain affects a significant percentage of the population. The American Shoulder and Elbow Surgeons Standardized Shoulder assessment form (ASES) is an outcome tool used to assess shoulder function, regardless of the disorder. However, at the moment the current study was undertaken, a Portuguese version of the ASES was not available. The objective of this work was to translate and make a cultural validation of the ASES to the Portuguese language. The original version of the ASES underwent the specific process of translation and cultural adaptation, comprising of the initial translation, back translation, committee, pre-test and the approval by the original author. The pre-test was applied in 20 patients with shoulder disorders (9 women, 41.1 ± 13.0 years of age, 11.2 ± 8.9 months with the disorder, and 12.5 ± 3.1 schooling years). The final Portuguese version of the ASES was established after patients considered all items of this tool comprehensible and clear, and the author of the original questionnaire considered it adequate. The results obtained with this study will help Brazilian rehabilitation professionals and researchers, since they have one more outcome measure to be applied in patients with functional disabilities of the shoulder.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".