Risk of Cancer-Specific Death for Patients Diagnosed With Neuroendocrine Tumors: A Population-Based Analysis
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.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".