Endoscopic submucosal dissection for early gastric cancer with undifferentiated-type histology: A meta-analysis
Bibliographic record
Abstract
AIM: To evaluate the efficacy and safety of endoscopic submucosal dissection (ESD) for early gastric cancer (EGC) with undifferentiated-type histology. METHODS: A systematic literature review was conducted using the core databases. Complete resection, curative resection, en bloc resection, recurrence and adverse event rate were extracted and analyzed. A random effect model was applied. The methodological quality of the enrolled studies was assessed using the Newcastle-Ottawa Scale. Publication bias was evaluated using a funnel plot, the trim and fill method, Egger's test, and a rank correlation test. RESULTS: Fourteen retrospective studies between 2009 and 2014 were identified (972 EGC lesions with undifferentiated-type histology). The total en bloc and complete resection rates were estimated as 92.1% (95%CI: 87.4%-95.2%) and 77.5% (95%CI: 69.3%-84%), respectively. The total curative resection rate was 61.4% (95%CI: 44.5%-75.9%). The overall recurrence rate was 7.6% (95%CI: 3.4%-16%). Limited to histologically diagnosed expanded-criteria lesions, the en bloc and complete resection rates were 91.2% and 85.6%, respectively. The curative resection rate was 79.8%. CONCLUSION: In this analysis, ESD is a technically feasible treatment modality for EGC with undifferentiated-type histology. Long-term studies are needed to confirm these therapeutic outcomes.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.030 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".