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Record W2098535905 · doi:10.1155/2012/173739

Canadian Association of Gastroenterology Consensus Guidelines on Safety and Quality Indicators in Endoscopy

2012· article· en· W2098535905 on OpenAlexaffvenueabout
David Armstrong, Alan Barkun, R. F. Bridges, Rose Carter, Chris de Gara, Catherine Dubé, Robert Enns, Roger Hollingworth, Donald MacIntosh, Mark Borgaonkar, Sylviane Forget, Grigorios I. Leontiadis, Jonathan B. Meddings, Peter B. Cotton, Ernst J. Kuipers, Roland Valori

Bibliographic record

VenueCanadian Journal of Gastroenterology · 2012
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsMemorial University of NewfoundlandDalhousie UniversityCredit Valley HospitalUniversity of AlbertaMcGill UniversityUniversity of CalgaryUniversity of British ColumbiaSt. Paul's HospitalMcMaster University
Fundersnot available
KeywordsMedicineGrading (engineering)VotingDelphi methodPatient safetyQuality (philosophy)Family medicineHealth careMedical educationMedical physicsComputer sciencePolitical science

Abstract

fetched live from OpenAlex

Several organizations worldwide have developed procedure-based guidelines and/or position statements regarding various aspects of quality and safety indicators, and credentialing for endoscopy. Although important, they do not specifically address patient needs or provide a framework for their adoption in the context of endoscopy services. The consensus guidelines reported in this article, however, aimed to identify processes and indicators relevant to the provision of high-quality endoscopy services that will support ongoing quality improvement across many jurisdictions, specifically in the areas of ethics, facility standards and policies, quality assurance, training and education, reporting standards and patient perceptions. BACKGROUND: Increasing use of gastrointestinal endoscopy, particularly for colorectal cancer screening, and increasing emphasis on health care quality, highlight the need for clearly defined, evidence-based processes to support quality improvement in endoscopy. OBJECTIVE: To identify processes and indicators of quality and safety relevant to high-quality endoscopy service delivery. METHODS: A multidisciplinary group of 35 voting participants developed recommendation statements and performance indicators. Systematic literature searches generated 50 initial statements that were revised iteratively following a modified Delphi approach using a web-based evaluation and voting tool. Statement development and evidence evaluation followed the AGREE (Appraisal of Guidelines, REsearch and Evaluation) and GRADE (Grading of Recommendations, Assessment, Development and Evaluation) guidelines. At the consensus conference, participants voted anonymously on all statements using a 6-point scale. Subsequent web-based voting evaluated recommendations for specific, individual quality indicators, safety indicators and mandatory endoscopy reporting fields. Consensus was defined a priori as agreement by 80% of participants. RESULTS: Consensus was reached on 23 recommendation statements addressing the following: ethics (statement 1: agreement 100%), facility standards and policies (statements 2 to 9: 90% to 100%), qual: 97% to 100%) and patient perceptions ( statements 22 and 23: 100%). Additionally, 18 quality indicators (agreement 83% to 100%), 20 safety indicators (agreement 77% to 100%) and 23 recommended endoscopy-reporting elements (agreement 91% to 100%) were identified. DISCUSSION: The consensus process identified a clear need for high-quality clinical and outcomes research to support quality improvement in the delivery of endoscopy services. CONCLUSIONS: The guidelines support quality improvement in endoscopy by providing explicit recommendations on systematic monitoring, assessment and modification of endoscopy service delivery to yield benefits for all patients affected by the practice of gastrointestinal endoscopy.

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)0.100
metaresearch head score (Gemma)0.160
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.964
Threshold uncertainty score0.710

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1000.160
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0040.007
Bibliometrics0.0210.021
Science and technology studies0.0050.006
Scholarly communication0.0080.004
Open science0.0100.007
Research integrity0.0100.012
Insufficient payload (model declined to judge)0.0100.005

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 head0.027
GPT teacher head0.297
Teacher spread0.270 · 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
GenreOther

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

Citations126
Published2012
Admission routes3
Has abstractyes

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