Association between surveillance imaging and survival outcomes in small bowel neuroendocrine tumors
Bibliographic record
Abstract
BACKGROUND: Surveillance guidelines following the resection of small bowel neuroendocrine tumors (SB-NETs) are inconsistent. We evaluated the impact of surveillance imaging on SB-NET recurrence and overall survival (OS). METHODS: Patients with completely resected SB-NETs referred to a provincial cancer center (2004-2015) were reviewed. Associations between imaging frequency, recurrence, post-recurrence treatment, and OS were determined using univariate and Cox-regression analyses. RESULTS: Among 195 completely resected SB-NET patients, 31% were ≥70 years, 43% were female, and 80% had grade 1 disease. Imaging frequency was predictive of recurrence (hazard ratio 2.52, 95% confidence interval 1.84-3.46, p < 0.001). 72% underwent interventions for recurrent disease. Patients who were treated for the recurrent disease had comparable OS to those who did not recur (median 152 vs. 164 months; p = 0.25). Imaging frequency was not associated with OS in those with treated recurrent disease (p = 0.65). Patients who recurred underwent more computerized tomography (CT) scans than those who did not recur (CT: 1.47 ± 0.89 vs. 1.02 ± 0.81 scans/year, p < 0.001). Detection of disease recurrence was 5%-7% per year during the first 6 years of surveillance and peaked at 17% in Year 9. CONCLUSION: Less frequent imaging over a longer duration should be emphasized to capture clinically relevant recurrences that can be treated to improve OS.
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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 | 0.004 |
| 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 | 0.000 | 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".