Endoscopic submucosal dissection for early gastric cancer with undifferentiated-type histology: A meta-analysis
Bibliographic record
Abstract
Background/Aims: Early gastric cancer (EGC) that is undifferentiated or shows submucosal invasion has not been generally accepted as an indication for endoscopic treatment.But recently, experiences with endoscopic submucosal dissection (ESD) for undifferentiated EGC or submucosal invasive (SM) EGC have increased.The aim of this study was to evaluate clinical outcomes of ESD for EGC with undifferentiation or submucosal invasion.Methods: Between August 2005 and August 2009, among 210 EGCs treated using ESD at our hospital, 18 lesions were diagnosed as undifferentiated gastric cancer and 41 as SM gastric cancer.A retrospective analysis was done on the medical records of these patients.Results: Mean follow-up periods were 19.39±11.2months.During the follow-up period, local recurrence was noted in 4 lesions.Local recurrence rates of the EGC groups (group 1, mucosal cancer with undifferentiation; group 2, SM cancer with differentiation; group 3, SM cancer with undifferentiation) were 10%, 4.5%, and 50%, respectively.Groups 1 and 2 were not significantly different in local recurrence rates compared to the mucosal cancer with differentiation group (p=0.061,p=0.125, respectively).The undifferentiated EGC group was significantly lower in curability using ESD than the differentiated EGC group (55.6% vs. 89.6%,p=0.000).The curability of the SM EGC group was lower than the mucosal EGC group (36.6% vs. 98.9%).Conclusions: Complete resection using ESD is difficult in undifferentiated and SM gastric cancers.SM cancer with undifferentiation should be treated immediately by salvage operation.For mucosal cancer with undifferentiation or SM cancer with differentiation, one should consider careful short-term follow-up.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".