Treatment and Mortality Following Cancer Diagnosis Among People With Non-affective Psychotic Disorders in Ontario, Canada: A Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND AND HYPOTHESIS: People with psychotic disorders have a higher risk of mortality following cancer diagnosis, compared to people without psychosis. The extent to which this disparity is influenced by differences in cancer-related treatment is currently unknown. We hypothesized that, following a cancer diagnosis, people with psychotic disorders were less likely to receive treatment and were at higher risk of death than those without psychosis. STUDY DESIGN: We constructed a retrospective cohort of cases of non-affective psychotic disorder (NAPD) and a general population comparison group, using Ontario Health (OH) administrative data. We identified cases of all cancers diagnosed between 1995 and 2019 and obtained information on cancer-related treatment and mortality. Cox proportional hazards models were used to compare the probability of having a consultation with an oncologist and receiving cancer-related treatment, adjusting for tumor site and stage. We also compared the rate of all-cause and cancer-related mortality between the two groups, adjusting for tumor site. STUDY RESULTS: Our analytic sample included 24 944 people diagnosed with any cancer. People with NAPD were less likely to receive treatment than people without psychosis (HR = 0.87, 95% CI = 0.82, 0.91). In addition, people with NAPD had a greater risk of death from any cause (HR = 1.68, 95% CI = 1.60, 1.76), compared to people without NAPD. CONCLUSIONS: The lower likelihood of receiving cancer treatment reflects disparities in accessing cancer care for people with psychotic disorders, which may partially explain the higher mortality risk following cancer diagnosis. Future research should explore mediating factors in this relationship to identify targets for reducing health disparities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".