Implementing neurodevelopmental follow‐up care for children with congenital heart disease: A scoping review with evidence mapping
Bibliographic record
Abstract
AIM: To identify and map evidence describing components of neurodevelopmental follow-up care for children with congenital heart disease (CHD). METHOD: This was a scoping review of studies reporting components of neurodevelopmental follow-up programmes/pathways for children with CHD. Eligible publications were identified through database searches, citation tracking, and expert recommendations. Two independent reviewers screened studies and extracted data. An evidence matrix was developed to visualize common characteristics of care pathways. Qualitative content analysis identified implementation barriers and enablers. RESULTS: The review included 33 studies. Twenty-one described individual care pathways across the USA (n = 14), Canada (n = 4), Australia (n = 2), and France (n = 1). The remainder reported surveys of clinical practice across multiple geographical regions. While heterogeneity in care existed across studies, common attributes included enrolment of children at high-risk of neurodevelopmental delay; centralized clinics in children's hospitals; referral before discharge; periodic follow-up at fixed ages; standardized developmental assessment; and involvement of multidisciplinary teams. Implementation barriers included service cost/resourcing, patient burden, and lack of knowledge/awareness. Multi-level stakeholder engagement and integration with other services were key drivers of success. INTERPRETATION: Defining components of effective neurodevelopmental follow-up programmes and care pathways, along with enhancing and expanding guideline-based care across regions and into new contexts, should continue to be priorities. WHAT THIS PAPER ADDS: Twenty-two different neurodevelopmental follow-up care pathways/programmes were published, originating from four countries. Twelve additional publications described broad practices for neurodevelopmental follow-up across regions Common attributes across eligibility, service structure, assessment processes, and care providers were noted. Studies reported programme acceptability, uptake, cost, and effectiveness. Implementation barriers included service cost/resourcing, patient burden, and lack of knowledge/awareness.
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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 | 0.029 | 0.111 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.025 | 0.018 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".