Psychiatric Comorbidities in Oncological Patients
Bibliographic record
Abstract
Introduction: Oncological patients have many psychiatric comorbidities. Whatever the therapy, the burden of the immediate, long term and late effects of these treatments adds to the inherent distress. Aims: The objective of the study was to assess the psychiatric comorbidities and the quality of life of oncological patients receiving chemotherapy and radiotherapy. Methods: 65 patients with different localization tumors were included in this observational study. Some receiving chemotherapy, some radiotherapy and some both. All patients were assessed using the Hospital Anxiety and Depression Scale (HADS) for anxiety and depressive symptoms, Montreal Cognitive Assessment (MOCA) for cognitive impairment and Quality of Life Enjoyment and Satisfaction Questionnaire – Short Form (Q-LES-Q-SF) for the quality of life before the oncological treatment and after one or three months of treatment. Patients with diagnosis criteria for depression or anxiety disorders received the recommended psychotropic treatment. Results: Patients with brain tumors receiving radiotherapy had lower scores on MOCA tests as opposed to patients receiving chemotherapy for any type of cancer that scored lower on the MOCA during their chemotherapy. Men scored more for anxiety and while women seemed more depressed but with better perspective on their outcome. The quality of life was correlated with the level of disability produced by the disease and treatment. 23 patients received antidepressant treatment during the study for depressive symptoms or anxiety. Patients receiving antidepressants showed better scores on HADS, MOCA and Q-LES-Q-SF scales. Conclusions: Psychiatric comorbidities are very frequent among oncological patients and can affect their quality of life. Antidepressant use among these patients could be neuroprotective and could improve their quality of life.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".