A systematic review of early motor interventions for infants with congenital heart disease and open-heart surgery
Bibliographic record
Abstract
BACKGROUND: Motor development delay is the first neurodevelopmental impairment that becomes apparent in infants with congenital heart disease (CHD). Early interventions have addressed high-risk groups like infants born preterm, but little is known about interventions to improve motor outcome in CHD infants at risk of motor delay. The purpose of this review was to systematically review the literature on type and effect of motor intervention applied during the first year of life in infants with CHD following open-heart surgery. METHODS: Scoping searches were performed in May 2020 and April 2023 via MEDLINE, Embase, CINAHL, Cochrane, PsycINFO, PEDro, and Scopus. The review included studies published in English from 2015 to 2022. Primary outcome was infants' motor development measured by standardized and non-standardized motor assessments, and if available, infants' language and cognitive development, and any parental quality-of-life assessments as secondary outcomes. The studies' quality was evaluated with a modified Newcastle-Ottawa scale. RESULTS: Four papers with low to high methodological quality met inclusion criteria. All studies investigated the influence of early physiotherapy. Four studies involved parents, and three studies used standardized tools to assess motor outcomes. No conclusion can be drawn about any positive effect of early motor interventions. CONCLUSIONS: Early motor intervention in CHD infants may improve motor development; however, the few existing studies do not provide clear evidence. Thus, more prospective early intervention studies are needed. TRIAL REGISTRATION: PROSPERO CRD42020200981.
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".