Upfront Small Bowel Resection for Small Bowel Neuroendocrine Tumors With Synchronous Metastases
Bibliographic record
Abstract
OBJECTIVE: We examined the impact of upfront small bowel resection (USBR) for metastatic small bowel neuroendocrine (SB-NET) compared to nonoperative management (NOM) on long-term healthcare utilization and survival outcomes. SUMMARY OF BACKGROUND DATA: The role of early resection of the primary tumor in metastatic SB-NET remains controversial. Conflicting data exist regarding its clinical and survival benefits. METHODS: This is a population-based retrospective matched comparative cohort study of adults diagnosed with synchronous metastatic SB-NET between 2001 and 2017 in Ontario. USBR was defined as resection within 6 months of diagnosis. Primary outcomes were subsequent unplanned acute care admissions and small bowel-related surgery. Secondary outcome was overall survival. USBR and NOM patients were matched 2:1 using a propensity-score. We used time-to-event analyses with cumulative incidencefunctions and univariate Andersen-Gill regression for primary outcomes. E value methods assessed the potential for residual confounding. RESULTS: Of 1000 patients identified, 785 had USBR. The matched cohort included 348 patients with USBR and 174 with NOM. Patients with USBR had lower 3-year risk of subsequent admissions (72.6% vs 86.4%, P < 0.001) than those with NOM, with hazard ratio 0.72 (95% confidence interval 0.570.91). USBR was associated with lower risk of subsequent small bowel-related surgery (15.4% vs 40.3%, P < 0.001), with hazard ratio 0.44 (95% confidence interval 0.29-0.67). E -values indicated it was unlikely that the observed risk estimates could be explained by an unmeasured confounder. Sensitivity analysis excluding emergent resections to define USBR did not alter the results. CONCLUSIONS: USBR for SB-NETs in the presence of metastatic disease was associated with better patient-oriented outcomes of decreased subsequent admissions and interventions, compared to NOM. USBR should be considered for metastatic SB-NETs.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".