Bibliographic record
Abstract
Background and Aims: Thulium laser is emerging as an alternative energy source in endourologic applications due to its precise cutting capabilities, reliable hemostasis, and limited thermal injury. These features have promoted its adoption in urology and sparked interest in gastrointestinal endoscopy. In endoscopic submucosal dissection (ESD), thulium laser offers potential advantages over conventional electrosurgical knives, including shallow penetration depth, powerful coagulation, and precise dissection. Methods: We describe the use of thulium laser-assisted colorectal ESD in a 68-year-old man with a 3.5-cm rectal polyp. Submucosal injection was followed by incision and complete dissection using the thulium laser. Results: En bloc thulium laser-assisted ESD of the rectal lesion was completed in 75 minutes without the requirement for coagulation forceps. There were no intraoperative or delayed adverse events. The final pathology was tubulovillous adenoma with clear margins. Conclusions: This case demonstrates the feasibility and potential benefits of thulium laser-assisted colorectal ESD. The precise cutting capabilities, shallow penetration depth, and effective coagulation properties of the thulium laser make it an exciting prospect as an alternative to conventional electrosurgical knives for colorectal ESD. Thulium laser-assisted ESD may offer a safe and effective alternative to conventional knives in colorectal ESD. Larger prospective studies are needed to confirm its safety and efficacy.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".