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Record W4413406300 · doi:10.1016/j.vgie.2025.07.013

Thulium laser–assisted colorectal endoscopic submucosal dissection

2025· article· en· W4413406300 on OpenAlexaff
Robert Bechara

Bibliographic record

VenueVideoGIE · 2025
Typearticle
Languageen
FieldMedicine
TopicGastric Cancer Management and Outcomes
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicineThuliumLaserOptics

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.018
GPT teacher head0.299
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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Same venueVideoGIESame topicGastric Cancer Management and OutcomesFrench-language works237,207