Schizophrenia and Cancer: In 2010 Do We Understand the Connection?
Bibliographic record
Abstract
OBJECTIVE: in recent years, there has been a plethora of cancer mortality and incidence data reported in schizophrenia. Despite this, there has been little focus on cancer in schizophrenia guidelines. Additionally, there have been suggestions that schizophrenia may provide inherent protection against cancer. The goal of this review is to establish, using recent data, the incidence and mortality rates for cancer in schizophrenia. METHOD: we identified systematic reviews and meta-analyses and undertook a search using the Medical Subject Headings' entry terms schizophrenia and neoplasm. RESULTS: incidence and mortality rates for cancer in schizophrenia are increased, compared with relevant general populations. Data are not uniformly reported and cohort ages tend to be young for expected cancer incidence. Despite the young cohort ages, the incidence of the major cancers-lung and breast-are substantially increased. Confounders are often not measured in the epidemiologic databases. When lung cancer is adjusted for smoking rates, there appears to be a lower risk of lung cancer than expected providing some basis to support an inherently reduced risk of cancer. There may also be a dissonance between incidence and mortality rates that suggest a prejudice against either diagnosis or treatment of these vulnerable patients. CONCLUSION: a single definitive study of schizophrenia and cancer is unfeasible, and future research will lean heavily on systematic review and meta-analysis. Researchers should report cancer data to include age and follow-up data and cohort overlap. Cancer accounts for almost an equivalent mortality as cardiovascular disease.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".