Avaliação de sintomas na distrofia muscular de Duchenne: uma estratégia de cuidado paliativo
Bibliographic record
Abstract
Symptom assessment, in Duchenne Muscular Dystrophy (DMD), allows an adequate treatment, and the Edmonton Symptom Assessment System (ESAS) assess it: evaluating clinical problems of patients in palliative care (pain, tiredness, drowsiness, nausea, appetite, shortness of breath, depression, anxiety, and well-being). Objective: To verify if patients with DMD understand the terms of the ESAS and if their symptoms could be assessed using this instrument. Methods: Ten patients with DMD were cross-sectional evaluated in relation to the understanding of the ESAS items, capacity to describe symptom (using the ESAS and the Faces Scale) and the Motor Function Measure. The patient’s symptom by ESAS was also classified by evaluator. A descriptive and correlation (Spearman's correlation coefficient) analysis of data was performed. Results: All patients understood the symptoms of pain, tiredness, drowsiness, depression (sadness), and well-being. However, some patients did not understand the symptoms of nausea, appetite, shortness of breath. The general mean of all symptoms assessed by the ESAS was below 5 points. For the symptom ‘depression’ and ‘anxiety’, a strong correlation was found between the results of the ESAS and the Face scale (r= 0.64, and r= 0.65, respectively). Additionally, a perfect and strong correlation, respectively, was found between the ESAS completed by the patient and the evaluator for the items '‘depression’," and ‘anxiety’ (r= 1.0)" and a ‘drowsiness’ (r= 0.82). Conclusion: DMD patients understood the ESAS terms and graded their symptoms using this instrument. Therefore, it is not necessary to change the ESAS terms to assess patients with DMD.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".