Influence of psychiatric comorbidity on quality of life in oncological patients
Bibliographic record
Abstract
Introduction: Quality of life is an important goal in the management of oncological patients. Quality of life can be influenced by numerous factors such as the level of functioning, age, as well as the presence of comorbid pathologies, both somatic and psychiatric. Objectives: The aim of this study is to measure the quality of life in a group of oncological patients, as well as the influence of various factors, particularly psychiatric comorbidities, on this variable. Materials and Methods: Data was obtained from a prospective, non-randomized, longitudinal study that was conducted over a 4-year span (2015-2018) and included 294 oncology patients. Patients were assessed by means of the following instruments: Hospital Anxiety and Depression Scale - HADS, Quality of Life Enjoyment and Satisfaction Questionnaire - QLESQ-SF, Visual Analog Scale for Pain - VAS Pain, CAGE Questionnaire for alcohol addiction, Global Assessment of Functioning Scale -GAF and Montreal Cognitive Assessment - MoCA for cognitive disorders. Results: Approximately 30% of patients included in the study obtained scores above the threshold for depression or anxiety on the HADS subscales, indicating an important association between cancer and the presence of depressive or anxious symptoms. Statistical analysis confirmed that the presence of depressive or anxious symptoms is a statistically significant predictor of quality of life. Quality of life is also influenced by the presence of cognitive impairment, alcohol use, and pain intensity as measured by the VAS Pain. Conclusions: Given that quality of life is influenced by the presence of psychiatric comorbidities (depression, anxiety, cognitive impairment, alcohol use), screening for these pathologies in all cancer patients and a multidisciplinary oncologist-psychiatrist-psychologist therapeutic approach are necessary
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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 | 0.000 |
| 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) | 0.000 | 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".