Type 2 and type 3 gastric neuroendocrine tumors have high risk of lymph node metastasis: 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
OBJECTIVES: Lymph node metastasis (LNM) is crucial in determining treatment strategies for gastric neuroendocrine tumors (gNETs). While type 3 is considered more aggressive than types 1 and 2 within the clinical subtype of gNETs, the supporting data were insufficient, due to their rarity. We aimed to study the prevalence and risk factors associated with LNM in gNETs. METHODS: We searched electronic databases from 1990 to 2023 to identify case-control and cohort studies regarding gNETs resected either endoscopically or surgically. The primary outcome measured was the pooled prevalence of LNM in gNETs. Secondary outcomes included categorizing the prevalence of LNM by clinical subtypes and identifying pathological risk factors associated with LNM in gNETs. RESULTS: We included 28 studies, involving 1742 patients, among whom 240 had LNM (pooled prevalence rate, 11.8%; 95% confidence interval 7.6-17.9%). The pooled prevalence rates of LNM for type 1, type 2, and type 3 gNETs were 6.0%, 38.5%, and 23.2%, respectively. Type 2 (odds ratio [95% confidence interval] 11.53 [3.46-38.49]) and type 3 (6.88 [3.79-12.49]) gNETs exhibited a higher risk for LNM compared to type 1. Pathological risk factors for LNM included tumor size >10 mm (4.18 [1.91-9.17]), tumor invasion into the muscularis propria or deeper (11.21 [3.50-35.92]), grade 2/grade 3 (5.96 [2.65-13.40]), and lymphovascular invasion (34.50 [6.70-177.51]). CONCLUSION: We demonstrated that type 2 gNETs, as well as type 3, had a high risk of LNM. Additionally, four pathological risk factors associated with LNM were identified.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| 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