Risk and predictors of suicide in colorectal cancer patients: A SEER analysis.
Bibliographic record
Abstract
9596 Background: Colorectal cancer (CRC) patients have a higher risk of suicide as compared with the general population. Due to differences in the sites/morbidity of recurrences as well as ostomy rates, we sought to evaluate the distribution and predictors of suicide among patients with colon and rectal cancer. Methods: A retrospective analysis was undertaken using the Surveillance, Epidemiology, and End Results (SEER) database from 1973-2009. Patients included were >18yrs and had confirmed adenocarcinoma of the colon or rectum. Results: Included in this analysis were 187,996 rectal cancer and 443,368 colon cancer patients. Colon cancer patients were older (median age 71 vs. 67 yrs, p <0.001) and included more females (51 vs. 43%, p <0.001) as compared to rectal cancer patients. Suicide rates were similar (611 [0.14%] vs. 337 [0.18%], p <0.001), as was the median time to suicide for colon vs. rectal cancer patients respectively (37 vs.32 months, p = 0.13). On univariate analysis, having rectal cancer was a predictor of suicide (HR 1.26; 95% CI: 1.10-1.43). However after adjustment for age, sex, race, marital, primary site surgery, stage and one primary, rectal cancer was not a predictor of suicide (HR 1.05; CI: 0.83- 1.33). In the combined CRC cohort, independent predictors of suicide included age >70 (HR 1.55; CI: 1.23-1.94), male gender (HR 7.56; CI: 5.34-10.70), being single (HR 1.56; CI: 1.14- 2.13), distant metastases at diagnosis (HR 1.58; CI: 1.13- 2.21), and white race (HR 3.21; CI: 1.75- 5.88). Also, lack of resection of primary tumor was associated with increased risk of suicide (HR 2.83; CI: 1.97- 4.05). Among colon cancer cohort, older age, male gender, and white race as well as lack of primary resection were independent predictors of suicide. Similarly, the aforementioned predictors as well as metastatic disease on presentation were the independent predictors of suicide in the rectal cohort. Conclusions: The suicide risk in CRC patients is low (< 0.2%) and no difference was found based on location of primary tumor. Gender, age, distant spread of disease, intact primary tumour and race are the main predictors of suicide among colorectal patients. Future studies and interventions are needed to target these high risk groups.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".