Cancer staging in individuals with a severe psychiatric illness: a cross-sectional study using population-based cancer registry data
Bibliographic record
Abstract
BACKGROUND: Advanced cancer stage at diagnosis may explain high cancer mortality among patients with a severe psychiatric illness (SPI). Studies to date investigating advanced stage cancer at diagnosis as a potential explanation for high cancer mortality in individuals with a history of mental illness have been inconclusive. We examined the relationship between a SPI history and unknown cancer stage at diagnosis in colorectal cancer (CRC) patients. METHODS: This was a population-based, cross-sectional study using linked administrative databases of CRC patients diagnosed between 01/04/2007 and 31/12/2012. Individuals who had a history of mental illness but did not meet the definition of a SPI were excluded. An SPI was measured in the 5 years prior to the cancer diagnosis and categorized as inpatient, outpatient or no SPI. Individuals with a best stage in Stage 0 to Stage IV were considered staged and absence of staging information was defined as unknown stage. The risk of unknown stage cancer was estimated using modified Poisson regression. RESULTS: The final study cohort included 24,507 CRC patients. 258 (1.1%) individuals experienced a history of inpatient SPI and 482 (2.0%) experienced outpatient SPI. After adjusting for confounders, CRC patients with an inpatient or outpatient history of SPI were at greater risk of having missing TNM stage at diagnosis, compared to patients with no history of a mental illness (RR 1.45 (95% CI: 1.14-1.85) and RR1.17 (95% CI 0.95-1.43), respectively). The results did not change when alternate practices to assign SPI history using administrative data were used. CONCLUSIONS: Individuals with an SPI, especially those with a psychiatric admission, were more likely to have missing stage data compared to individuals without a history of a mental illness. Incomplete and low quality cancer staging data likely undermines the quality of cancer care following initial diagnosis. Understanding why patients with an SPI are missing this information is a critical first step to providing excellent care to this vulnerable population.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".