MétaCan
Menu
Back to cohort
Record W3194137696 · doi:10.1097/mpg.0000000000003262

Overview of the Pediatric Endoscopy Quality Improvement Network Quality Standards and Indicators for Pediatric Endoscopy

2021· review· en· W3194137696 on OpenAlexafffund
Catharine M. Walsh, Jenifer R. Lightdale, David R. Mack, Jorge Amil Dias, Patrick Bontems, Herbert Brill, Nicholas M. Croft, Douglas S. Fishman, Raoul I. Furlano, Peter M. Gillett, Iva Hojsak, Matjaž Homan, Hien Q. Huynh, Kevan Jacobson, Ian Leibowitz, Diana G. Lerner, Quin Y. Liu, Petar Mamula, Priya Narula, Salvatore Oliva, Matthew R. Riley, Joel R. Rosh, Marta Tavares, Elizabeth C. Utterson, Lusine Ambartsumyan, Anthony Otley, Robert Krämer, Veronik Connan, Graham A. McCreath, Mike Thomson

Bibliographic record

VenueJournal of Pediatric Gastroenterology and Nutrition · 2021
Typereview
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsDalhousie UniversityBC Children's HospitalUniversity of British ColumbiaUniversity of AlbertaWilliam Osler Health SystemMcMaster UniversityMcMaster Children's HospitalChildren's Hospital of Eastern OntarioUniversity of TorontoSickKids FoundationUniversity of OttawaThe Wilson CentreStollery Children's HospitalHospital for Sick Children
FundersUniversity of Ottawa
KeywordsMedicineGuidelineBenchmarkingQuality managementGrading (engineering)Quality (philosophy)Observational studyAuditDelphi methodMedical physicsQuality assuranceOperations managementPathologyExternal quality assessmentAccounting

Abstract

fetched live from OpenAlex

INTRODUCTION: Pediatric-specific quality standards for endoscopy are needed to define best practices, while measurement of associated indicators is critical to guide quality improvement. The international Pediatric Endoscopy Quality Improvement Network (PEnQuIN) working group was assembled to develop and define quality standards and indicators for pediatric gastrointestinal endoscopic procedures through a rigorous guideline consensus process. METHODS: The Appraisal of Guidelines for REsearch and Evaluation (AGREE) II instrument guided PEnQuIN members, recruited from 31 centers of various practice types representing 11 countries, in generating and refining proposed quality standards and indicators. Consensus was sought via an iterative online Delphi process, and finalized at an in-person conference. Quality of evidence and strength of recommendations were rated according to the GRADE (Grading of Recommendation Assessment, Development, and Evaluation) approach. RESULTS: Forty-nine quality standards and 47 indicators reached consensus, encompassing pediatric endoscopy facilities, procedures, endoscopists, and the patient experience. The evidence base for PEnQuIN standards and indicators was largely adult-based and observational, and downgraded for indirectness, imprecision, and study limitations to "very low" quality, resulting in "conditional" recommendations for most standards (45/49). CONCLUSIONS: The PEnQuIN guideline development process establishes international agreement on clinically meaningful metrics that can be used to promote safety and quality in endoscopic care for children. Through PEnQuIN, pediatric endoscopists and endoscopy services now have a framework for auditing, providing feedback, and ultimately, benchmarking performance. Expansion of evidence and prospective validation of PEnQuIN standards and indicators as predictors of clinically relevant outcomes and high-quality pediatric endoscopic care is now a research priority.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.341
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.049
GPT teacher head0.382
Teacher spread0.333 · 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.

Study designObservational
Domainnot available
GenreReview

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

Citations24
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Pediatric Gastroenterology and NutritionSame topicColorectal Cancer Screening and DetectionFrench-language works237,207