Are There Ways To Prevent Psychiatric Affections In Oncological Patients?
Bibliographic record
Abstract
Background and Aims:Psycho-oncology gives an approach to cancer patients and treats the emotional, social and spiritual distress which accompanies them. Recent research has indicated that not only illness, but also treatment can lead to severe depression, anxiety and distress. This study aims to establish the prevalence of anxiety and depression, the quality of life, cognitive impairment, sleep disorders and substance dependence in cancer patients from a Romanian hospital. It also describes the clinical characteristics of these patients and examines if different types of cancer have any influence on the level of psychiatric diagnosis.Methods:This is a prospective, longitudinal study that followed 130 patients from the radio-oncology department for three months and 37 of them were reevaluated. For the evaluation of psychiatric comorbidities a number of eight scales were used: Hospital Anxiety and Depression Scale, Quality of Life Questionnaire, Athens Insomnia Scale, Numeric Rating Scale for Pain, CAGE scale and Fagerstrom test, Global Assessment of Functioning and Montreal Cognitive Assessment.Results:As expected, depression and anxiety are underdiagnosed among the lot of the study. The quality of life is corelated with 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 the consume after confronting the diagnosis.Conclusions:To sum up, patients from oncology department should have access to psychiatric services due to 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".