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Record W7118265228 · doi:10.4103/sjg.sjg_333_25

Saudi Gastroenterology Association (SGA) clinical care pathway and standards of care for peroral endoscopic myotomy (POEM) in achalasia

2025· article· en· W7118265228 on OpenAlexaff
Resheed Alkhiari, M Almadi, Fahad Alsohaibani, Majid Alsahafi

Bibliographic record

VenueSaudi Journal of Gastroenterology · 2025
Typearticle
Languageen
FieldMedicine
TopicGastroesophageal reflux and treatments
Canadian institutionsMcGill UniversityMcGill University Health CentreMontreal General Hospital
Fundersnot available
KeywordsAchalasiaMyotomyClinical pathwayCare pathwayQuality managementPatient carePatient safety

Abstract

fetched live from OpenAlex

Graphical Abstract Export Peroral endoscopic myotomy (POEM) has become an established, minimally invasive therapy for achalasia, demonstrating high efficacy and safety across all disease subtypes. With its expanding adoption in Saudi Arabia, the Saudi Gastroenterology Association (SGA) developed a clinical care pathway and standards of care to promote safe, consistent, and high-quality practice. The pathway provides evidence-based guidance encompassing all phases of care: preprocedural, intraprocedural, and postprocedural. Key covered aspects include diagnostic evaluation, patient selection, preparation, procedural techniques, documentation, and postprocedural follow up. The document also outlines requirements for endoscopist training and privileges to ensure procedural safety and optimal quality. Quality metrics were formulated to support performance monitoring and continuous improvement across all phases of care. By defining clear standards, this pathway aims to standardize clinical practice, enhance procedural safety, optimize therapeutic outcomes, and elevate the overall quality of care delivered to achalasia patients undergoing POEM.

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)0.009
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.035
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0150.004

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 head0.011
GPT teacher head0.336
Teacher spread0.325 · 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 designNot applicable
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

Citations0
Published2025
Admission routes1
Has abstractyes

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