The risk of lymph node metastases and their impact on survival in patients with appendiceal neuroendocrine neoplasms: a systematic review and meta-analysis of adult and paediatric patients
Bibliographic record
Abstract
BACKGROUND: There are no clear histopathological parameters determining the risk of lymph node (LN) metastases and appropriateness of completion prophylactic right hemicolectomy (RHC) in patients with appendiceal neuroendocrine neoplasms (ANENs). MATERIALS AND METHODS: The PubMed, Cochrane Library, Embase, Web of Science and SCOPUS databases were searched up to November 2018. Quality/risk of bias was assessed using the Newcastle-Ottawa Scale (NOS). RESULTS: A total of 526 articles were screened. In 11 adult and 3 paediatric studies, 602 and 77 unique patients, respectively, with ANEN and undergoing RHC, were included. The rate of LN metastases for a cutoff size >10 mm was 48.6% (vs 12.1% for lesions <10 mm) among adult patients, with an odds ratio (OR) of 4.8 (95% CI, 1.5-15.8). For 20 mm size cutoff, these figures were 61% (vs 28.2% for lesions <20 mm) with an OR of 3.2 (95% CI, 1.3-7.8). Vascular-, lymph vessel- and perineural invasions were identified as predictive factors for LN metastases in adult patients. In paediatric patients, there were no strong morphological predictors for LN metastases. The 10-year disease-specific survival (DSS) for adult patients without LN metastases was 99.2% vs 95.6% in patients with LN (OR: 0.2; 95% CI, 0.02-2.4). The complication rate of prophylactic RHC was 11.4%. CONCLUSIONS: This meta-analysis demonstrates that tumour size >20 mm as well as >10 mm and/or vascular-, lymph vessel- and perineural invasions are associated with increased risk for LN metastases in adult patients with ANEN. The prognostic value of LN positivity remains to be determined in further studies with long-term follow-up.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.024 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".