Bibliographic record
Abstract
Comment on: Innovations for colonic endoscopic submucosal dissection: combination of the latest game changers Endoscopy 2023; 55(S 01): E1172-E1173 DOI: 10.1055/a-2191-5546 10.1055/a-2191-5546 For over 20 years, multiple techniques and tools have evolved for the improvement of endoscopic submucosal dissection (ESD); nevertheless, optimizing mucosal lifting and access to the submucosal space remain fundamentally important. But the crucial elements of ESD performance can be obscured by the elaborations – “the devil is in the details.” In this video, Pioche et al. present how some simple technical features, developed over time or shrewdly adjusted, have become instrumental in enhancing ESD performance. The use of a flex knife that enables replacement of conventional injection by high pressure injection with viscous solution, together with novel multipolar traction that can be varied as needed by the operator during the procedure, allows better access to the submucosal space [ 11 ] [ 22 ]. Additionally, the more precise visualization of deep-lying vessels and bleeding points by red dichromatic imaging also positively influences the procedure time and R0 achievement [ 33 ]. Ergonomic improvement by fixing the positions of the three foot pedals is another of the seemingly insignificant details, along with adequate scope selection and flexibility, that is beneficial in optimizing ESD performance. These enhancements may be especially useful when risk factors for unsuccessful or challenging ESD persist, including large size of the lesion, difficult location, deeper submucosal invasion, or severe fibrosis [ 44 ]. These proposed tools may allow easier and more successful performance of ESD. Publication History Article published online: 28 February 2024 © 2024. Thieme. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany
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 | 0.009 | 0.088 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.079 | 0.057 |
| Insufficient payload (model declined to judge) | 0.041 | 0.030 |
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".