Pediatric endoscopy: how can we improve patient outcomes and ensure best practices?
Bibliographic record
Abstract
INTRODUCTION: Strategies to promote high-quality endoscopy in children require consensus around pediatric-specific quality standards and indicators. Using a rigorous guideline development process, the international Pediatric Endoscopy Quality Improvement Network (PEnQuIN) was developed to support continuous quality improvement efforts within and across pediatric endoscopy services. AREAS COVERED: This review presents a framework, informed by the PEnQuIN guidelines, for assessing endoscopist competence, granting procedural privileges, audit and feedback, and for skill remediation, when required. As is critical for promoting quality, PEnQuIN indicators can be benchmarked at the individual endoscopist, endoscopy facility, and endoscopy community levels. Furthermore, efforts to incorporate technologies, including electronic medical records and artificial intelligence, into endoscopic quality improvement processes can aid in creation of large-scale networks to facilitate comparison and standardization of quality indicator reporting across sites. EXPERT OPINION: PEnQuIN quality standards and indicators provide a framework for continuous quality improvement in pediatric endoscopy, benefiting individual endoscopists, endoscopy facilities, and the broader endoscopy community. Routine and reliable measurement of data, facilitated by technology, is required to identify and drive improvements in care. Engaging all stakeholders in endoscopy quality improvement processes is crucial to enhancing patient outcomes and establishing best practices for safe, efficient, and effective pediatric endoscopic care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".