Early movement patterns of preterm infants with cerebral palsy (CP)
Bibliographic record
Abstract
Background: Premature infants are at risk of developing adverse neurological outcomes, including cerebral palsy (CP). Early detection facilitates timely intervention, optimizing neuroplasticity and function. The General Movement Assessment (GMA) offers a cost-effective means of identifying cerebral palsy by observing infants’ general movements abnormality. Objectives: In this project, we utilized GMA data (68 observations for CP and 92 observations for controls) to classify significant general movements and group infants potentially affected by cerebral palsy at an early stage. Methods: We evaluated the effectiveness of general movements in predicting cerebral palsy by metrics such as accuracy and Cohen’s kappa. Supervised and unsupervised machine learning models were employed for prediction. Results: Fidgety Movements in Typical movement exhibit the highest accuracy at 84% (95% CI: [78%, 89%]), as well as the highest kappa value at 0.67 (95% CI: [0.55, 0.79]) among all variables, indicating excellent capability in distinguishing CP from normal infants. In supervised learning, a logistic model identified an association between general movements and a diagnosis of CP. The model achieved an overall accuracy of 93% (95% CI: [88%, 97%]) and an overall cross-validated accuracy of 90% (95% CI: [66%, 98%]), with a sensitivity of 86% and specificity of 93%. In unsupervised learning, K-prototypes clustering naturally grouped all observations into three clusters with accuracy 87%. One cluster was made up of 85% controls and the other two clusters consisted of 96% and 84% CP. Conclusions: The GMA can predict cerebral palsy with high accuracy, and significant movements correlated with a diagnosis of cerebral palsy, such as Fidgety movements, Kicking, Asymmetry of Finger Posture, were identified.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".