INTERNATIONAL COLUMN: Association of demographic, economic and clinical variables in daily activities and symptoms presented by patients in cancer treatment
Bibliographic record
Abstract
OBJECTIVE: To investigate the association between demographic, economic and clinical variables, cancer symptoms, and daily life interference in patients receiving cancer treatment in Brazil. METHODS: In this cross-sectional study, 268 patients were assessed. A questionnaire was used to collect data on demographic, economic and clinical variables, and the M.D. Anderson Symptom Inventory was used to assess cancer symptoms. Data were analyzed using bivariate and multivariate descriptive statistics. FINDINGS: The following variables were associated with higher symptom scores: female sex (prevalence ratio [PR]=1.28; 95% confidence interval [95% CI] 1.06-1.53), illiteracy or ≤ 9 years of formal education (PR=1.40; 95% CI 1.08-1.82), clinical equipment or situations that requiring nursing care (PR=1.23; 95% CI 1.03-1.46), and family history of cancer (PR=1.23; 95% CI 1.04-1.45). Daily life interference was associated with female sex (PR=1.40; 95% CI 1.12-1.75), secondary tumour (PR=1.42; 95% CI 1.16-1.74) and radiotherapy (PR=1.24; 95% CI 1.01-1.51). CONCLUSION: Management of cancer patients requires multidisciplinary knowledge, taking into consideration all the subjective dimensions of the patients. Knowing the profile of patients most strongly affected by symptoms will help them face the limitations and consequences of the disease and its treatment.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 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".