Management of psychiatric-related comorbidities in people with cancer
Bibliographic record
Abstract
Introduction: Psycho-oncology provides a comprehensive understanding of cancer patients and offers an approach to the emotional, social and spiritual distress which accompanies them. Objectives: This study aims to estimate the prevalence of anxiety and depression, the quality of life, cognitive impairment, sleep disorders and substance dependence in cancer patients from a general Romanian hospital. Materials & Methods: This is a prospective longitudinal study that assessed 130 patients from the radio-oncology department for three months. A number of 37 patients were reevaluated. We applied a number of eight scales for the evaluation of psychiatric comorbidities: Hospital Anxiety and Depression Scale (HADS), Quality of Life Enjoyment and Satisfaction Questionnaire (QLESQ-SF), Athens Insomnia Scale (AIS), Visual Analog Scale for Pain (VAS Pain), CAGE scale and Fagerstrom Test (for alcohol and tobacco dependence), Global Assessment of Functioning (GAF) and Montreal Cognitive Assessment (MoCA; for cognitive impairment). Results: As expected, depression and anxiety rated high in the study population. The quality of life is strongly correlated with the intensity of pain, depression and anxiety level. There was a high rate of alcohol and nicotine use among these oncological patients, although the majority of them stopped consume after confronting with the diagnosis. Patients with lung cancer have frequent sleep disorders. Conclusions: To sum up, patients from oncology departments should always have access to psychiatric services due to the high prevalence of this type of disorders. Both the disease and oncological treatment influence the quality of life and can lead to anxiety and depression.
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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 | 0.000 |
| 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) | 0.001 | 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".