Superior Performance in Prone in Infants With Congenital Heart Disease Predicts an Earlier Onset of Walking
Bibliographic record
Abstract
Infants with congenital heart disease are at risk of impaired neurodevelopment, which frequently manifests as motor delay during their first years of life. This delay is multifactorial in origin and environmental factors, such as a limited experience in prone, may play a role. In this study, we evaluated the motor development of a prospective cohort of 71 infants (37 males) with congenital heart disease at 4 months of age using the Alberta Infant Motor Scales (AIMS). We used regression analyses to determine whether the 4-month AIMS scores predict the ability to walk by 18 months. The influence of demographic and clinical variables was also assessed. Fifty-one infants (71.8%) were able to maintain the prone prop position (AIMS score of ≥3 in prone) at 4 months. Of those, 47 (92.2%) were able to walk by 18 months compared to only 12/20 (60%) of those who did not maintain the position. Higher AIMS scores were predictive of a greater likelihood of walking by 18 months ( P < .001), with the scores in prone having a higher predictive ability compared to those in other positions (Exp(B) 15.2 vs 4.0). Shorter hospital stays and female gender were also associated with an earlier onset of walking. In conclusion, our study demonstrates that early ventral performance in infants with congenital heart disease impacts the age of acquisition of walking and could be used to guide referral to rehabilitation.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".