Prevalence and risk factors for lymph node metastasis in duodenal neuroendocrine tumors: a systematic review and meta-analysis
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
BACKGROUND: Although the status of lymph node metastasis (LNM) is crucial in determining treatment strategy for duodenal neuroendocrine tumors (D-NETs), robust evidence for their potential LNM risk remains lacking. This systematic review aimed to summarize the prevalence and risk factors of LNM in D-NETs. METHODS: This systematic review of electronic databases identified eligible case-control and cohort studies for D-NET resected either endoscopically or surgically, published from 1990 to 2023. The primary outcome was the pooled prevalence of LNM in D-NETs. Secondary outcomes included the pooled prevalence of LNM according to tumor location and functionality, as well as identifying pathological risk factors for LNM. Meta-analysis was performed. RESULTS: We identified 36 studies that involved 1,396 patients with D-NETs, including 326 with LNM. The pooled prevalence of LNM in D-NETs was 22.7% (95% confidence interval [CI] 17.3-29.2%). The prevalence was high in ampullary/peri-ampullary D-NETs and functional D-NETs (46.8 and 53.3%, respectively), whereas it was low in non-functional, non-ampullary D-NETs (NAD-NETs) (9.5%). Pathological risk factors for LNM in NAD-NETs included tumor size > 10 mm (odds ratio [OR] 7.31 [95% CI 3.28-16.31]), tumor invasion into the muscularis propria or deeper (OR 7.79 [3.65-16.61]), lymphovascular invasion (OR 5.67 [2.29-14.06]), and World Health Organization grading of G2 (OR 2.47 [1.03-5.92]). CONCLUSION: Approximately one-fourth of the patients with D-NETs had LNM. Endoscopic resection might be acceptable for non-functional NAD-NETs with diameters of 10 mm or less, but additional surgical resection with lymphadenectomy may be recommended for cases exhibiting pathological risk factors.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.016 | 0.003 |
| Bibliometrics | 0.002 | 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) | 0.000 | 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