Associations between mental illness and cancer: a systematic review and meta-analysis of observational studies.
Bibliographic record
Abstract
OBJECTIVE: Considering the impact of mental illness and cancer on the society, the relationship between the two diseases should be assessed. This study aimed at determining the association between mental illness and cancer. MATERIALS AND METHODS: The Embase and Medline databases were searched on October 21, 2020. Cohort, case-control, and cross-sectional studies were eligible for study inclusion. The Newcastle-Ottawa scale was used to qualitatively assess the risk of bias. Funnel plots were drawn to evaluate the risks of bias across the included studies. RESULTS: We included 58 studies from 16 countries, incorporating approximately 30 national databases and 25 million individuals. Patients with psychiatric disorders did not show an increased risk of developing cancer. However, patients with cancer had a significantly increased risk of developing mental illness. The survival rates of patients with mental illness according to cancer occurrence and patients with cancer according to mental illness occurrence were significantly decreased. CONCLUSIONS: Clinicians should conduct early screening to ensure that appropriate interventions for mental illness are administered in patients with cancer. Due to the high incidence of death in patients with mental illnesses due to unnatural causes, such as suicide, homicide, and accidents, clinicians should be aware of the importance of the treatment and management of these patients.
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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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.018 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".