Prognosis of Patients With Colorectal Cancer and Apical Lymph Node Metastasis at the Inferior Mesenteric Artery: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: This review was designed to compile the evidence on the prognosis of patients with colorectal cancer and apical lymph node (APN) metastasis and the long-term benefit of inferior mesenteric artery lymph node (IMA-LN) resection. METHODS: We searched the PubMed Central, Cochrane library, EMBASE, and MEDLINE databases from inception until May 2021 for relevant publications. We assess the quality of the studies using the Newcastle Ottawa scale. We conducted a random-effects model meta-analysis and report pooled hazard ratios (HRs) with 95% confidence intervals (CIs). RESULTS: We analyzed data from 13 studies conducted in Japan, China, and Korea with 6,193 participants. Most studies were retrospective in nature and of low quality. We found that patients with APN metastasis had shorter OSs (pooled HR, 2.41; 95% CI, 1.92-3.02) and PFSs (pooled HR, 2.42; 95% CI, 1.90-3.09) than the patients without the metastasis. We identified significant heterogeneity without publication bias for both outcomes. Moreover, our sensitivity analysis revealed robust estimates were robust for the individual effects. CONCLUSION: Our findings suggest that patients with colorectal cancer and APN metastases have significantly worse OS and DFS than those without the metastasis. However, inclusion of low-quality retrospective studies with high heterogeneity limits the generalizability of study findings.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.022 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".