Canadian Developmental Follow-up Practices in Children With Congenital Heart Defects: A National Environmental Scan
Bibliographic record
Abstract
Background: Developmental follow-up is central to the timely identification of delays in at-risk children. Throughout Canada, data are currently lacking on the follow-up of children with congenital heart disease (CHD) after open-heart surgery. The objective of this study was to describe current Canadian developmental follow-up practices and to explore barriers to optimal follow-up. Methods: A cross-sectional study was implemented with health professionals involved with the developmental follow-up of children with CHD in the 8 specialized hospitals that perform pediatric open-heart surgery in Canada. A questionnaire collected descriptive information about the setting and current follow-up practices. In addition, an interview was conducted to explore what would be considered optimal developmental follow-up in Canada and identify potential barriers. Results: Four of the 8 tertiary care centres had a systematic developmental follow-up program that included screening and formal evaluation. These programs were only accessible to a subset of children with CHD identified to be at higher risk. Participants described current practices as suboptimal and aimed to develop a more systematic developmental follow-up program or expand an existing one. Participants emphasized the lack of human resources, financial supports, and limited dedicated time as major barriers to offering optimal follow-up care. Conclusions: Current follow-up practices in Canada are considered suboptimal by health care specialists involved in treating children with CHD. These practices may fail to promptly identify children and adolescents with CHD who have developmental challenges. It is essential that we develop national recommendations to optimize the developmental follow-up practices in Canada for this high-risk population.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".