Excess Cancer Mortality in Psychiatric Patients
Bibliographic record
Abstract
Objectives: There are conflicting data on cancer incidence and mortality in psychiatric patients, although most studies suggest that while cancer mortality is higher, incidence is no different from that in the general population. Different methodologies and outcomes may account for some of the conflicting results. We investigated the association between mental illness and cancer incidence, first admission rates, and mortality in Nova Scotia using a standard methodology. Method: A population-based record-linkage study of 247 344 patients in contact with primary care or specialist mental health services during 1995 to 2001 was used. Records were linked with cancer registrations and death records. Results: Cancer mortality was 72% higher in males (95%CI, 63% to 82%) and 59% higher in females (95%CI, 49% to 69%) among patients in contact with mental health services. This was reflected in similarly elevated first admission rates. However, there was weaker and less consistent evidence for increased incidence. For several cancer sites, incidence rate ratios were lower than might be expected given the mortality and first admission rate ratios, and no higher than that of the general population. These were melanoma, prostate, bladder, and colorectal cancers in males. Conclusion: People with mental illness in Nova Scotia have increased mortality from cancer, which cannot always be explained by increased incidence. Possible explanations for further study include delays in detection or initial presentation leading to more advanced staging at diagnosis, and difficulties in communication or access to health care.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".