A Survey of Immunization Practices in Patients With Congenital Heart Disease
Bibliographic record
Abstract
Background Congenital heart disease, the most common congenital anomaly, often presents in neonates. Because of perceived risks, health care providers may consider deferring immunizations in this population. We sought to understand the perceived risk of immunizations in those providing health care to children with particular heart conditions. Methods A survey, which included 6 hypothetical scenarios assessing immunization recommendations, was distributed internationally to relevant health care providers, and responses were compared between the different scenarios. Results Majority of responses (n = 142) were from paediatric cardiologists (n = 98; 69%) and nurse practitioners (n = 27; 19%) located in the United States (n = 77; 54%) or Canada (n = 53; 37%) working in academic teaching hospitals (n = 133; 93.7%). Most favoured vaccinations (n = 107; 75.4%) and less likely to proceed with the first immunization in infants with structural heart disease compared with channelopathy (risk ratio: 0.80, confidence interval: 0.73-0.87; P < 0.001). Only 40% would proceed with immunization as normal in an infant with manifest Brugada type I electrocardiogram. Special precautions after the immunization included longer duration of observation (19%) and administering prophylactic antipyretic medication (92%). Conclusions Respondents were 20% more likely to defer immunizations in the presence of treatable structural heart disease as compared with channelopathy despite the lack of evidence supporting deferring immunizations in children with structural heart disease. Most were cautious in their response to the scenario involving Brugada syndrome, indicating awareness of the risk of haemodynamic instability in the event of a fever. The majority of respondents still strongly recommend immunizations in this population as the benefits outweigh the potential for adverse events.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".