Risk of cancer-specific death for patients diagnosed with neuroendocrine tumors: A population-based analysis.
Bibliographic record
Abstract
4605 Background: While patients with neuroendocrine tumours (NETs) are known to experience prolonged overall survival, the contribution of cancer-specific and non-cancer deaths is undefined. We examined cancer-specific and non-cancer death after NET diagnosis. Methods: We conducted a population-based retrospective cohort study of adult patients with NETs from 2001-2015 by linking administrative healthcare datasets. Using competing-risks methods, we estimated the cumulative incidence of cancer-specific and non-cancer death and stratified by primary NET site and metastatic status. Sub-distribution hazard models examined prognostic factors. Results: Among 8,607 included patients, median follow-up was 42 months (interquartile range: 17-82). The risk of cancer-specific was higher than that of non-cancer death, with 27.3% (95%CI: 26.3-28.4%) and 5.6% (95%CI: 5.1-6.1%) at 5 years. Cancer-specific deaths largely exceeded non-cancer deaths in synchronous and metachronous metastatic NETs. Patterns varied by primary tumour site, with highest risks of cancer-specific death in broncho-pulmonary and pancreatic NETs. For non-metastatic gastric, small intestine, colonic, and rectal NETs, the risk of non-cancer 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 from cancer is higher than that of dying from other causes. Heterogeneity exists by primary NET site. Some patients with non-metastatic NETs are more likely to die from non-cancer than from cancer causes. This information is important for counselling, decision-making, and design of future trials. Cancer-specific mortality should be included in outcomes when assessing treatment strategies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".