Predictive Factors for Local Recurrence and Incomplete Resection of Early Gastric Cancer Treated by Endoscopic Resection: A Western Experience
Bibliographic record
Abstract
BACKGROUND: Early gastric cancer (EGC) is defined as adenocarcinoma limited to the mucosa or submucosa regardless of lymph node involvement. Local EGC recurrence rates have been described in up to 6% of cases. OBJECTIVES: To evaluate predictive factors for incomplete resection and local recurrence of EGC treated by endoscopic mucosal resection (EMR) that was followed up for at least one year. METHODS: From June 1994 to December 2005, 46 patients with EGC underwent EMR. Possible predictive factors for incomplete endoscopic resection and local recurrence were identified by medical chart analysis. Demographic, endoscopic and histopathological data were retrospectively evaluated. EMR was considered complete or incomplete. Patients from the complete resection group were divided into subgroups (with and without local EGC recurrence). RESULTS: Complete resection was possible in 36 cases (76.6%). Predictive factors for incomplete resection were tumour location (P=0.035), histological type (P=0.021), lesion size (P=0.022) and number of resected fragments (P=0.013). On multivariate analysis, undifferentiated histological type (OR 0.8; 95% CI 0.036 to 0.897) and number of resected fragments (OR 7.34; 95% CI 1.266 to 42.629) were independent predictive factors for incomplete resection. In the complete resection group, a larger lesion size was associated with a higher the number of resected fragments (P=0.018). Local recurrence occurred in nine cases (25%). Use of the cap technique was the only predictive factor for local recurrence in five of seven cases (71.4%) (P=0.006). CONCLUSIONS: A larger lesion size was associated with a higher number of resected fragments. Undifferentiated adenocarcinoma and piecemeal resection were predictive factors for incomplete resection. Technique type was a predictive factor for local EGC recurrence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".