Impacts of COVID-19 on pediatric patients with congenital heart disease: a small systematic and integrative literature review
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to compile primary studies to understand the impacts of COVID-19 on pediatric patients with congenital heart disease. DATA SOURCE: A systematic review based on the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) method, with searches conducted in the PubMed, Latin American and Caribbean Health Sciences Literature (LILACS), and Scientific Electronic Library Online (SciELO) databases. Studies published in the last 5 years, open access, and addressing the research question, "What are the main impacts of COVID-19 on pediatric patients with congenital heart disease?" were included. The risk of bias was assessed using the Newcastle-Ottawa Scale (NOS) and the Joanna Briggs Institute (JBI) tools. DATA SYNTHESIS: A total of 377 articles were identified, of which 12 met the inclusion criteria. The NOS tool indicated that two of the eight cohort studies had a risk of bias and lower methodological quality. The JBI tool revealed that three of the four cross-sectional studies had a low risk of bias and good methodological quality. The integrative analysis highlighted three main impacts of COVID-19 on these patients: difficulties in follow-up and treatment, reduced physical activity due to social distancing, and postponement of procedures and surgeries. Infected patients experienced increased complications and hospitalizations, but without a significant change in mortality. CONCLUSIONS: The COVID-19 pandemic significantly affected the health and management of congenital heart disease, leading to clinical complications and worsening follow-up. Further primary and secondary studies are needed to strengthen the evidence and improve patient management.
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 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 | 0.002 | 0.181 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".