A influência do vídeo de informação adicional em pacientes submetidas à mastectomia: o estudo da dor
Bibliographic record
Abstract
OBJECTIVE: Pain is a high incidence symptom in cancer patients. In cases of breast cancer, the post mastectomy and post axillary curettage pain syndromes present the characteristic of neural pain and can occur in 10% of patients shortly after surgery. This paper presents the preliminary results of an ongoing research aimed at investigating the possible effects of a modern procedure of information, which used in the evaluation of perception of pain in mastectomy patients at the Mastectomy Center of the Hospital de Clínicas de Porto Alegre, southern Brazil. MATERIALS AND METHODS: A sample of 22 patients was studied according to the classical experimental design with two groups. An informational video was shown to the experimental group in the pre-surgery period. Both groups were evaluated using the McGill Pain Questionnaire and the Visual Analogue Scale pain score. RESULTS: Results indicated a tendency to reports of more post-surgical pain in the control group in both tests. In some of the subscales of the McGill Questionnaire there were significantly higher scores of post-surgical pain in the control group. CONCLUSIONS: Our findings seem to indicate the benefits of using informational videos with the objective of improving the well-being of patients. However, further studies should be carried out with larger population samples in order to better evaluate the trends observed in this study
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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 | 0.002 | 0.038 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".