Colorectal endoscopic submucosal dissection in the USA: The current state and future perspectives
Bibliographic record
Abstract
Endoscopic submucosal dissection (ESD) is a transformative advancement in the endoscopic management of superficial gastrointestinal lesions. Initially conceived for the treatment of early gastric cancer, ESD has demonstrated proficiency in achieving en-bloc resection of superficial gastrointestinal lesions. ESD has experienced widespread acceptance in Japan and East Asia; however, its adoption in the USA remains delayed. This initial hesitancy could be attributed to procedural complexity and training demands; nonetheless, recently, ESD has been gaining popularity in the USA. This is due to the advancements in endoscopic technology, tailored training programs, and cumulative evidence regarding the efficacy and safety of ESDs. This review aimed to deliberate the historical progress, current implementation, and prospective trajectory of ESDs in the USA. With ongoing clinical research, technological integration, and educational efforts, ESD is likely to become the gold standard for managing large gastrointesitinal lesions. This progress marks an imperative step toward less invasive, more precise, and patient-centric approaches regarding advanced therapeutic endoscopy in the USA.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".