Changes in Survival Outcomes of Patients With Neuroendocrine Neoplasms Over the Past 15 Years
Bibliographic record
Abstract
BACKGROUND: The past 2 decades have observed a number of advances in therapeutic approaches to patients with neuroendocrine neoplasms (NENs). This study aims to assess whether survival outcomes have changed among patients with NENs over the past 15 years, in a real-world, population-based study. MATERIALS AND METHODS: We accessed administrative databases within the province of Alberta, Canada, and we reviewed patients with invasive NENs diagnosed 2004 to 2019. Patients were classified according to the year of diagnosis into 3 groups: 2004 to 2008; 2009 to 2013; and 2014 to 2019. Kaplan-Meier survival estimates were used to compare overall survival (OS) according to different baseline characteristics (including the year of diagnosis). Multivariable Cox regression modeling was used to examine factors associated with the risk of death in this cohort. RESULTS: We included a total of 3431 patients in the study cohort. Using multivariable Cox regression analysis, the following factors were associated with worse survival: older age at diagnosis (hazard ratio [HR]: 3.45; 95% CI [confidence interval]: 2.74-4.35), male sex (HR: 1.38; 95% CI: 1.21-1.56), lung primary site (HR for lung vs. appendicular primary: 1.39; 95% CI: 1.01-1.92), Stage 4 disease (HR: 2.80; 95% CI: 2.38-3.30), South zone of the province (HR for South zone vs. Calgary zone: 1.85; 95% CI: 1.49-2.30), and higher comorbidity index (HR for ≥3 vs. 0: 2.66; 95% CI: 2.19-3.24). Although Kaplan-Meier method showed significant difference in OS according to diagnosis period, multivariable regression model showed that the period of diagnosis did not appear to impact OS (HR for diagnosis period 2004 to 2009 vs. 2014 to 2019: 1.04; 95% CI: 0.89-1.22). CONCLUSIONS: Over the study period (2004 to 2019), patients diagnosed during later periods did not appear to experience better OS compared with patients diagnosed at an earlier time.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".