Anaesthetic complications associated with the treatment of patients with congenital cardiac disease: consensus definitions from the Multi-Societal Database Committee for Pediatric and Congenital Heart Disease
Bibliographic record
Abstract
Abstract Congenital heart defects are the most common cause of death in infants and young children in the developed world. As the mortality in this population has declined to less than 5%, more attention is being focused now on reducing post-procedural morbidities that may seriously impact the patient and their families. Because of multiple reasons, paediatric cardiac surgery and anaesthesia is a perfect model for studying human errors and their impact on patient safety. Congenital cardiac disease is a common lesion causing much morbidity, pain, and loss of life. Over 44,000 surgical procedures are performed yearly to repair congenital cardiac problems in the United States alone. The reduction or elimination of iatrogenic adverse outcomes, given the current mortality rates of 4.2%–4.5%, might lead to as many as 500 children achieving better outcomes or shorter hospitalizations. Efforts to quantify the frequency of complications related to anaesthesia in patients undergoing congenital cardiac surgery have been difficult to date because of the low occurrence of this surgery compared to other surgeries on children and the relatively rare incidence of complications related to anaesthesia in this population. Anaesthesiologists play a crucial role in the reduction, recognition, and timely treatment of medical errors that impact this morbidity. Paediatric cardiac surgery encompasses many complex procedures that are highly dependent upon a sophisticated organizational structure, effective communication, coordinated efforts of multiple individuals working as a team, and high levels of cognitive and technical performance. Human factor error analysis in this patient population has shown how frequently both minor and major errors occur. The goal of this paper is to outline the frequency and sources of these errors and to suggest treatment strategies which may minimize their occurrence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".