Perda de peso em pacientes oncológicos: prevalência e prognóstico relacionados a sexo, idade, localização do tumor e sintomas de impacto nutricional
Bibliographic record
Abstract
ABSTRACT Introduction: Cancer causes an increase in nutritional demands and the presence of of nutritional impact symptoms (NIS) contributes to reduction of nutrient intake and absorption, leading to weight loss, malnutrition and predicting overall survival. Assess the prevalence and predict the weight loss related to cancer, the Grade Scheme and the NIS. Methods: Data were collected from 2012 to 2018 from the first nutritional consultation of cancer patients in a clinic linked to SUS in the city of Guarapuava-PR. The primary outcome was to determine the % of weight loss (% WL), NIS and by the Grade Scheme proposed by Martin et. al (2015) the prognosis was determined by univariate and multivariate Multinomial Logistic Regression (MLR) analysis (adjusted for age, sex and tumor location). Results: 1164 patients aged 56.9 years. In the first consultation, a 6.7 %WL was observed, and it was observed that 21.6% of the patients were underweight. The main sites and %WL were, respectively: Lung 140 (12.0%) and 9.4 %WL, Head and Neck 113 (9.7%) and 10.5 %WL, Colorectal 84 (7.2%) and 10.3 %WL, Stomach 90 (7.7%) and 13.7 %WL, Esophagus 85 (7.3%) and 14.0 %WL, Pancreas 24 (2.1%) and 16.1 %WL. The main NIS were: dry mouth (51.0%), abdominal pain (23.0%), constipation (21.7%), nausea (15.3%) and altered taste (10.5%). In the RML for univariate analysis, age, sex, cancer site and SIN and for multivariate analysis, all cancer locations showed significant OR to be classified in grades 3 and 4. Conclusion: Before chemotherapy, weight loss and malnutrition are present. The cancer site and SIN increase the chance of the patient being classified in grades 3 and 4, leading these patients to a worse nutritional status and contributing to adverse results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".