Predictive factors for lymph node metastasis in early gastric cancer with signet ring cell histology: a meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Less invasive surgery is widely used in the treatment of early gastric cancer; however, no definite guidelines exist regarding indications for less invasive surgery to treat early gastric cancer with signet ring cell histology. The aim of this study was to identify risk factors for lymph node metastasis (LNM) in early signet ring cell carcinoma (SRC). An extensive search of PubMed, Embase and the Cochrane library was performed for pertinent articles involving early SRC and LNM. METHODS: Eligible data (gender, depth of invasion, lymphovascular invasion, size, ulceration, macroscopic type and location) were extracted from the included studies and systematically reviewed via a meta-analysis. Review Manager version 5.3 was used to perform the data processing. The Newcastle-Ottawa Scale was utilized to evaluate the quality of the included articles. RESULTS: Fourteen studies were included in the final analysis. After meta-analysis, female gender, submucosal invasion, lymphovascular invasion and size >20 mm were associated with LNM in early SRC. CONCLUSION: Four variables were identified as risk factors for LNM in early SRC. The significance of the results of the present study should be further confirmed in more early SRC patients for future clinical use.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.029 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".