Handling and Pathology Reporting of Gastrointestinal Endoscopic Mucosal Resection
Bibliographic record
Abstract
Endoscopic mucosal resection (EMR) is a non-invasive alternative to surgery that is now frequently used for resection of early lesions in both upper and lower parts of the gastrointestinal (GI) tract. One of the main advantages of these techniques is providing tissue for histopathological examination. Pathological examination of endoscopically resected specimens of GI tract is a crucial component of these procedures and is useful for prediction of both the risk of metastasis and lymph node involvement. As the first step, it is very important for the pathologist to handle the EMR gross specimen in the correct way: it should be oriented, and then the margins should be labeled and inked accurately before fixation. In the second step, the EMR pathological report should include all the detailed information about the diagnosis, grading, depth of invasion (mucosa only or submucosal involvement), status of the margins, and the presence or absence of lymphovascular invasion. The current literature (PubMed and Google Scholar) was searched for the words “endoscopic mucosal resection” to find all relevant publications about this technique with emphasis on the pathologist responsibilities.
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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 | 0.008 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".