ReviewAmerican heart journal2016
Rationale and methodology of a collaborative learning project in congenital cardiac care.
Review in American heart journal, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 32 citations in OpenAlex.
- Utilizing a Collaborative Learning Model to Promote Early Extubation Following Infant Heart Surgery.Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies · 2016Trial
- Artificial Intelligence in Congenital Heart Disease: Current State and Prospects.JACC. Advances · 2022Review
- The role of machine learning applications in diagnosing and assessing critical and non-critical CHD: a scoping review.Cardiology in the young · 2021Article
- A Collaborative Learning Assessment of Developmental Care Practices for Infants in the Cardiac Intensive Care Unit.The Journal of pediatrics · 2020Article
- Changes in Anesthetic and Postoperative Sedation-Analgesia Practice Associated With Early Extubation Following Infant Cardiac Surgery: Experience From the Pediatric Heart Network Collaborative Learning Study.Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies · 2019Article
- Hospital Costs Related to Early Extubation After Infant Cardiac Surgery.The Annals of thoracic surgery · 2019Article
- Variation in Implementation and Outcomes of Early Extubation Practices After Infant Cardiac Surgery.The Annals of thoracic surgery · 2019Article
- Extubation Failure Rates After Pediatric Cardiac Surgery Vary Across Hospitals.Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies · 2019Article
- Differences in Cost of Care by Palliation Strategy for Infants With Ductal-Dependent Pulmonary Blood Flow.Circulation. Cardiovascular interventions · 2019Article
- A Learning Collaborative Approach Increases Specificity of Diagnosis of Acute Liver Failure in Pediatric Patients.Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association · 2018Observational
- Duration of Postoperative Mechanical Ventilation as a Quality Metric for Pediatric Cardiac Surgical Programs.The Annals of thoracic surgery · 2018Article
- Artificial Intelligence versus Doctors' Intelligence: A Glance on Machine Learning Benefaction in Electrocardiography.Discoveries (Craiova, Romania) · 2017Review
- Mortality and Morbidity after Laparoscopic Surgery in Children with and without Congenital Heart Disease.The Journal of pediatrics · 2017Article
- Artificial intelligence in pediatric cardiology and cardiac surgery: Irrational hype or paradigm shift?Annals of pediatric cardiologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 7 institutions in 1 country.
Funding
Abstract
backgroundCollaborative learning is a technique through which individuals or teams learn together by capitalizing on one another's knowledge, skills, resources, experience, and ideas. Clinicians providing congenital cardiac care may benefit from collaborative learning given the complexity of the patient population and team approach to patient care. RATIONALE AND DEVELOPMENT: Industrial system engineers first performed broad-based time-motion and process analyses of congenital cardiac care programs at 5 Pediatric Heart Network core centers. Rotating multidisciplinary team site visits to each center were completed to facilitate deep learning and information exchange. Through monthly conference calls and an in-person meeting, we determined that duration of mechanical ventilation following infant cardiac surgery was one key variation that could impact a number of clinical outcomes. This was underscored by one participating center's practice of early extubation in the majority of its patients. A consensus clinical practice guideline using collaborative learning was developed and implemented by multidisciplinary teams from the same 5 centers. The 1-year prospective initiative was completed in May 2015, and data analysis is under way.
conclusionCollaborative learning that uses multidisciplinary team site visits and information sharing allows for rapid structured fact-finding and dissemination of expertise among institutions. System modeling and machine learning approaches objectively identify and prioritize focused areas for guideline development. The collaborative learning framework can potentially be applied to other components of congenital cardiac care and provide a complement to randomized clinical trials as a method to rapidly inform and improve the care of children with congenital heart disease.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.