Rol del distrés psicológico en la relación entre percepción de enfermedad y calidad de vida en pacientes con cáncer de mama
Bibliographic record
Abstract
Objective: To evaluate the associations between the illness perception dimensions and quality of life, assessing the modulatory role of psychological distress in patients with breast cancer, identifying which of these dimensions explained further variability in the different aspects of the quality of life. Methods: Seventy-five patients were evaluated with the Brief Illness Perception Questionnaire and the European Organization for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire (QLQ-30). We calculated bivariate and partial correlations to evaluate the associations between the illness perception dimensions and different aspects of quality of life, controlling for a distress measure. Subsequently, we performed linear regression analysis to evaluate the illness perception dimensions that could explain the variability in the quality of life scores. Results: Although significant associations between subscales of perception of illness and quality of life were found, most of them lost their significance when controlled by distress. In the regression models, variables that best predicted the variability in the quality of life were psychopathological diagnostic and distress. Conclusions: According with the study results, psychological distress and psychopathological diagnostic were the two variables that explained better the variability in the quality of life. For this reason it is essential to learn more about the role of these variables on the quality of life and morbidity and mortality associated with them.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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 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".