Stage at diagnosis and survival in patients with cancer and a pre-existing mental illness: a meta-analysis
Bibliographic record
Abstract
Introduction Individuals with a pre-existing mental illness, especially those experiencing reduced social, occupational and functional capacity, are at risk for cancer care disparities. However, uncertainty surrounding the effect of a mental illness on cancer outcomes exists. Methods We conducted a systematic review and meta-analysis of observational studies using MEDLINE and PubMed from 1 January 2005 to 1 November 2018. Two reviewers evaluated citations for inclusion. Advanced stage was defined as regional, metastatic or according to a classification system. Cancer survival was defined as time survived from cancer diagnosis. Pooled ORs and HRs were presented. The Newcastle-Ottawa bias risk assessment scale was used. Random-effects models used the Mantel-Haenszel approach and the generic inverse variance method. Heterogeneity assessment was performed using I 2 . Results 2381 citations were identified; 28 studies were included and 24 contributed to the meta-analysis. Many demonstrated methodological flaws, limiting interpretation and contributing to significant heterogeneity. Data source selection, definitions of a mental illness, outcomes and their measurement, and overadjustment for causal pathway variables influenced effect sizes. Pooled analyses suggested individuals with a pre-existing mental disorder have a higher odds of advanced stage cancer at diagnosis and are at risk of worse cancer survival. Individuals with more severe mental illness, such as schizophrenia, are at a greater risk for cancer disparities. Discussion This review identified critical gaps in research investigating cancer stage at diagnosis and survival for individuals with pre-existing mental illness. High-quality research is necessary to support quality improvement for the care of psychiatric patients and their families during and following a cancer diagnosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".