Second primary cancers and survival among neuroendocrine tumor 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
There is an increased risk of second primary cancers (SPCs) after neuroendocrine tumor (NET) diagnosis. The clinical significance of SPCs in this population is unknown. The purpose of this study was to evaluate the association between SPCs after NET diagnosis and survival. We performed a population-based, retrospective cohort study of NET patients (gastrointestinal, pancreatic, or lung primary) from 2000 to 2016 using the Surveillance, Epidemiology, and End Results database. Cox regression models assessed the association between SPCs and NET-specific (NET-SS), cancer-specific (CSS), and overall survival (OS). Of 58,553 NET patients, 7.9% experienced an SPC. SPCs were associated with worse OS (hazard ratio (HR) 2.14, 95% CI 1.94-2.36) and CSS (HR 2.31, 95% CI 2.06-2.59) with no difference in NET-SS (HR 1.04, 95% CI 0.87-1.23). Stratified analyses by histologic grade showed similar results for well and moderately differentiated NETs, but no difference in OS or CSS for poorly differentiated NETs (P > 0.05). In stratified analyses by NET site, SPCs were associated with worse OS (HR 3.41, 95% CI 3.01-3.87) and CSS (HR 4.96, 95% CI 4.28-5.74) in gastrointestinal NETs and worse OS (HR 1.25, 95% CI 1.03-1.52) with no difference in CSS (HR 1.08, 95% CI 0.85-1.36) in lung NETs. SPCs were not associated with a difference in OS or CSS in pancreatic NETs (P > 0.05). In conclusion, SPCs after NETs were associated with inferior OS and CSS compared to no SPC but were not associated with NET-SS. These data highlight the need for long-term follow-up in NETs to include the detection of SPCs to ensure early diagnosis and timely management.
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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) | 0.001 | 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 | 0.001 |
| 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 it