Differences in gross motor and fine motor outcomes for toddlers after early complex cardiac surgery
Bibliographic record
Abstract
OBJECTIVES: To determine whether gross motor scores of toddlers after complex cardiac surgery were different from fine motor scores and were adequately represented by motor composite scores and, whether acute care predictors and chronic childhood health markers of gross motor scores differed from those of fine motor. METHODS: This prospective inception-cohort outcomes study included 171 toddlers after complex cardiac surgery with cardiopulmonary bypass at age <6 months, born in Northern Alberta from 2009 to 2019, and without known chromosomal abnormalities. At a mean (standard deviation) age of 21.7 (3.7) months, the Bayley Scales of Infant and Toddler Development-III determined motor composite and scaled scores (normative values, 100 (15), 10 (3), respectively). The same variables from surgery and assessment were analysed using multivariate regression to predict gross and fine motor scores; results expressed as effect size (95% confidence interval) with % variance. RESULTS: Composite, fine, and gross motor scores were 89.7 (14.2), 9.4 (2.5), and 7.2 (2.7), respectively. Predictive variables accounted for 21.2% of the variance for fine motor, and 36.9% for gross motor. Multivariate analysis for gross motor scores included toddlers need for cardiac medication, effect size (95% confidence interval) -0.801 (-1.62, -0.02), gastrostomy, -1.35 (-2.39, -0.319), and single ventricle, -0.93 (-1.71, -0.15). These same variables did not predict fine motor scores. CONCLUSION: Gross motor skills commonly were lower than fine motor skills for toddlers after complex cardiac surgery. Predictors for gross motor scores differed from fine motor scores. Separate reporting of gross motor scores could lead to improved identification of predictors of delay and to optimised early intervention.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".