The Impact of Preexisting Severe Mental Disorders on Cancer Mortality: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
ABSTRACT Purpose People with severe mental disorders (SMD) face a significantly lower life expectancy compared to people without SMD. Studies have reported divergent results concerning cancer‐specific mortality. Therefore this systematic review and meta‐analysis aimed to assess the cancer‐specific mortality for people with preexisting SMD. Methods A comprehensive literature search was conducted across PubMed, Embase, Psycinfo, and Scopus for studies published since January 2003. Inclusion criteria targeted adult cancer patients with a known SMD diagnosis prior to their cancer diagnosis. Two authors independently screened records based on predefined criteria, resolving discrepancies through discussion. Data extraction and quality assessment were conducted using the Newcastle‐Ottawa Scale. A random effects model was employed to conduct the analysis, with heterogeneity across the studies quantified using the I 2 statistic. Results The search yielded 4736 records, of which 25 studies met the eligibility criteria. Findings consistently indicated higher cancer‐specific mortality among patients with preexisting SMD, with a 1.37 (95% CI: 1.30–1.44) higher relative risk of cancer‐specific mortality for patients with preexisting SMD. The highest mortality rates were found among patients with schizophrenia and other psychosis with a relative cancer mortality risk at 1.47 (95% CI: 1.33–1.63). Conclusion This review and meta‐analysis highlighted a concerning higher relative cancer‐specific mortality risk for patients with preexisting SMD. These findings underscore the need for integrated healthcare approaches addressing both cancer treatment and mental health to improve outcomes for this vulnerable population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.039 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".