Risk of Cancer-Specific Death for Patients Diagnosed With Neuroendocrine Tumors: A Population-Based Analysis
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Although patients with neuroendocrine tumors (NETs) are known to have prolonged overall survival, the contribution of cancer-specific and noncancer deaths is undefined. This study examined cancer-specific and noncancer death after NET diagnosis. METHODS: We conducted a population-based retrospective cohort study of adult patients with NETs from 2001 through 2015. Using competing risks methods, we estimated the cumulative incidence of cancer-specific and noncancer death and stratified by primary NET site and metastatic status. Subdistribution hazard models examined prognostic factors. RESULTS: Among 8,607 included patients, median follow-up was 42 months (interquartile range, 17-82). Risk of cancer-specific death was higher than that of noncancer death, at 27.3% (95% CI, 26.3%-28.4%) and 5.6% (95% CI, 5.1%-6.1%), respectively, at 5 years. Cancer-specific deaths largely exceeded noncancer deaths in synchronous and metachronous metastatic NETs. Patterns varied by primary tumor site, with highest risks of cancer-specific death in bronchopulmonary and pancreatic NETs. For nonmetastatic gastric, small intestine, colonic, and rectal NETs, the risk of noncancer death exceeded that of cancer-specific deaths. Advancing age, higher material deprivation, and metastases were independently associated with higher hazards, and female sex and high comorbidity burden with lower hazards of cancer-specific death. CONCLUSIONS: Among all NETs, the risk of dying of cancer was higher than that of dying of other causes. Heterogeneity exists by primary NET site. Some patients with nonmetastatic NETs are more likely to die of noncancer causes than of cancer causes. This information is important for counseling, decision-making, and design of future trials. Cancer-specific mortality should be included in outcomes when assessing treatment strategies.
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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 | 0.001 |
| 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 | 0.000 | 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