Population-based comparison of cancer survival outcomes in patients with and without psychiatric disorders
Bibliographic record
Abstract
BACKGROUND: Individuals with psychiatric disorders (PD) have a high prevalence of tobacco use. Patients with PD also potentially receive substandard care in comparison to the general population. Previous research has shown that individuals with PD have a decreased risk of receiving a tobacco related (TR) cancer diagnosis. To further assess this trend, this study assesses the survival of patients with a TR cancer with or without a PD. MATERIALS AND METHODS: to identify people in British Columbia that have been diagnosed with psychiatric disorders and appendicitis (our control group). From these groups, we selected individuals who also had a TR cancer. We subsequently extracted information pertaining to these patients from these databases. RESULTS: Thirty-nine thousand eight hundred forty-one patients with cancer were included in our study. Analyses of these patients were controlled for by age, gender, cancer type and diagnosis year. This analysis displayed shorter survival time among patients who were diagnosed with depression (HR = 1.16; p = 0.01; 95% CI: 1.04-1.29), schizophrenia (HR = 1.62; p < 0.01; 95% CI: 1.43-1.84), or bipolar disorder (HR = 1.35; p < 0.01; 95% CI: 1.12-1.64) compared to the cancer patients without a PD, all of which were statistically significant. People that were diagnosed with anxiety disorders did not have a survival time that was significantly different from our control population (HR = 1.07; p = 0.22; 95% CI: 0.96-1.19). CONCLUSIONS: Individuals with PD, except for those with anxiety, were found to have a shorter survival time following diagnosis with a TR cancer as compared to our control group. We hypothesize several factors, which may account for this statistically significant difference: (1) delayed diagnosis, (2) poor access to care, (3) poor assessment or follow-up, or (4) physician beliefs of poor treatment adherence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".