ESD, not EMR, should be the first-line therapy for early gastric neoplasia
Bibliographic record
Abstract
With interest, we read the insightful recommendations by Banks et al 1 and the British Society of Gastroenterology on the management of precancerous conditions and lesions in the stomach. They rightly identify that the management of these conditions lacks consistency not only in the UK but also in the majority of Western societies.2 With a growing appreciation for quality indicators in upper GI endoscopy,3 these guidelines are an essential resource for both general endoscopists and tissue resection specialists. Nevertheless, despite the increasing expertise in endoscopic submucosal dissection (ESD) outside of Japan,4 we were surprised that endoscopic mucosal resection (EMR) was recommended for lesions ≤10 mm. This is in contrast to recommendations by the Japan Gastroenterological Endoscopy Society (JGES)5 and the European Society of Gastrointestinal Endoscopy (ESGE).6 Three systematic reviews7–9 have compared ESD versus EMR for early gastric cancer (EGC). In …
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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 | 0.002 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.022 | 0.024 |
| Insufficient payload (model declined to judge) | 0.009 | 0.011 |
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".