Comparative Risk Factors for Accidental and Suicidal Death in Cancer Patients
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 it