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Record W2783985204 · doi:10.1055/s-0043-122229

A novel method of endoscopic-assisted esophageal clearance in advanced achalasia

2018· article· en· W2783985204 on OpenAlexaff
Shinwa Tanaka, Fumiaki Kawara, Takashi Toyonaga, Robert Bechara, Namiko Hoshi, Hirofumi Abe, Yoshiko Ohara, Tsukasa Ishida, Yoshinori Morita, Eiji Umegaki

Bibliographic record

VenueEndoscopy International Open · 2018
Typearticle
Languageen
FieldMedicine
TopicEsophageal and GI Pathology
Canadian institutionsQueen's University
Fundersnot available
KeywordsAchalasiaMedicineGeneral surgeryEsophagusSurgery

Abstract

fetched live from OpenAlex

BACKGROUND AND STUDY AIMS: In order to perform peroral endoscopic myotomy (POEM) safely, retained liquid and food debris must be removed before the procedure is started. We developed a novel technique using a super-slim gastroscope, and a gastric tube to remove retained food debris in achalasia patients. In this study, the safety and efficacy of this novel technique were investigated. PATIENTS AND METHODS: Eleven patients with achalasia were enrolled in this study and underwent this novel method for esophageal clearance. RESULTS: All patients had complete clearance of the retained food debris using this method. The median procedure time (range) was 13 (6 - 30) minutes. There were no serious adverse events (AEs) and one minor AE of mucosal erythema due to mucosal suctioning. CONCLUSION: This novel method for esophageal clearance is safe and effective in achalasia patients with large amounts of retained food debris.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.033
GPT teacher head0.396
Teacher spread0.363 · 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 designCase report
Domainnot available
GenreMethods

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

Citations6
Published2018
Admission routes1
Has abstractyes

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