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Record W2600573601 · doi:10.1097/mpg.0000000000001592

Quality Improvement in Pediatric Endoscopy

2017· article· en· W2600573601 on OpenAlexaff
Robert Krämer, Catharine M. Walsh, Diana G. Lerner, Douglas S. Fishman

Bibliographic record

VenueJournal of Pediatric Gastroenterology and Nutrition · 2017
Typearticle
Languageen
FieldMedicine
TopicEsophageal and GI Pathology
Canadian institutionsSickKids FoundationThe Wilson CentreHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineToolboxEndoscopyQuality (philosophy)Health careQuality managementMEDLINERealmBest practiceExpert opinionMedical physicsSurgeryIntensive care medicineOperations managementManagement system

Abstract

fetched live from OpenAlex

The current era of healthcare reform emphasizes the provision of effective, safe, equitable, high-quality, and cost-effective care. Within the realm of gastrointestinal endoscopy in adults, renewed efforts are in place to accurately define and measure quality indicators across the spectrum of endoscopic care. In pediatrics, however, this movement has been less-defined and lacks much of the evidence-base that supports these initiatives in adult care. A need, therefore, exists to help define quality metrics tailored to pediatric practice and provide a toolbox for the development of robust quality improvement (QI) programs within pediatric endoscopy units. Use of uniform standards of quality reporting across centers will ensure that data can be compared and compiled on an international level to help guide QI initiatives and inform patients and their caregivers of the true risks and benefits of endoscopy. This report is intended to provide pediatric gastroenterologists with a framework for the development and implementation of endoscopy QI programs within their own centers, based on available evidence and expert opinion from the members of the NASPGHAN Endoscopy Committee. This clinical report will require expansion as further research pertaining to endoscopic quality in pediatrics is published.

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)0.001
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 score0.009
Threshold uncertainty score0.351

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.019
GPT teacher head0.311
Teacher spread0.291 · 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

Citations28
Published2017
Admission routes1
Has abstractyes

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