MétaCan
Menu
Back to cohort
Record W3042907732 · doi:10.1055/a-1218-6089

Snare-tip soft coagulation is effective and efficient as a first-line modality for treating intraprocedural bleeding during Barrett’s mucosectomy

2020· article· en· W3042907732 on OpenAlexaff
Sergei Vosko, Sunil Gupta, Neal Shahidi, Luke F. Hourigan, W. Arnout van Hattem, Iddo Bar-Yishay, Scott Schoeman, Mayenaaz Sidhu, Nicholas G. Burgess, Eric Y.T. Lee, Michael J. Bourke

Bibliographic record

VenueEndoscopy · 2020
Typearticle
Languageen
FieldMedicine
TopicGastric Cancer Management and Outcomes
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineHemostasisSurgeryHemostaticsEndoscopy

Abstract

fetched live from OpenAlex

BACKGROUND : Intraprocedural bleeding (IPB) during multiband mucosectomy (MBM) for Barrett's neoplasia can obscure the endoscopic field. Current hemostatic devices may affect procedure continuity and technical success. Snare-tip soft coagulation (STSC) as a first-line therapy for primary hemostasis has not previously been studied in this setting. METHODS: Between January 2014 and November 2019, 191 consecutive patients underwent 292 MBM procedures for Barrett's neoplasia within a prospective observational cohort in two tertiary care centers. A standard MBM technique was performed. IPB was defined as bleeding obscuring the endoscopic field that required intervention. The primary outcome was the technical success and efficacy of STSC. RESULTS: IPB occurred in 63 MBM procedures (21.6 %; 95 % confidence interval 17.3 % - 26.7 %). STSC was attempted as first-line therapy in 51 IPBs, with the remainder requiring alternate therapies because of pooling of blood. STSC achieved hemostasis in 48 cases (94.1 % by per-protocol analysis; 76.2 % by intention-to-treat analysis). No apparatus disassembly was required to perform STSC. CONCLUSIONS: STSC is a safe, effective, and efficient first-line hemostatic modality for IPB during MBM for Barrett's neoplasia.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.403
Threshold uncertainty score0.701

Codex and Gemma teacher scores by category

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.286
Teacher spread0.269 · 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 teacher head, 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

Citations5
Published2020
Admission routes1
Has abstractyes

Explore more

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