Comparative Risk Factors for Accidental and Suicidal Death in Cancer Patients
Bibliographic record
Abstract
BACKGROUND: Cancer patients appear at higher risk of accidental death and suicide. The reasons for this and how suicide and accidental death relate remain unclear. AIMS: To clarify and contrast risk factors for such deaths among cancer patients. METHODS: A SEER (1973-2007) analysis was conducted on 4,449,957 cancer patients comparing all causes of death (COD) to accidental and suicidal deaths through competing hazards, relative risk and proportional hazards models. SEER did not provide psychological assessments; the analysis was confined to their standard epidemiological and clinicopathological parameters. RESULTS: 2,557,385 overall deaths yielded 16,879 (0.66%) accidents and 6,589 (0.26%) suicides. Mortality reached its highest incidence immediately after diagnosis and obeyed Pareto type II distributions. The major identifiable risk factor for suicide was male gender; for accidental death, First Nations ethnicity; and all COD, metastases. Minor factors for suicide included metastases, advanced age, and respiratory as well as head and neck tumors, whereas for accidental death they were male gender, metastases, advanced age, and brain tumors. CONCLUSIONS: Differences were observed in the risk patterns of suicide and accidental death, suggesting distinct etiologies. A high incidence of suicides and accidental deaths following diagnosis (attributed by some to stress from the diagnosis of cancer) correlated here with overall mortality and indicators of physical morbidity. Cancer patients with the above identifiable risk factors warrant supportive attention.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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, 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".