Predicting Motor Development in Very Preterm Infants at 12 Months’ Corrected Age: The Role of Qualitative Magnetic Resonance Imaging and General Movements Assessments
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to compare the predictive value of qualitative MRI of brain structure at term and general movements assessments at 1 and 3 months' corrected age for motor outcome at 1 year's corrected age in very preterm infants. PATIENTS AND METHODS: Eighty-six very preterm infants (<30 weeks' gestation) underwent MRI at term-equivalent age, were evaluated for white matter abnormality, and had general movements assessed at 1 and 3 months' corrected age. Motor outcome at 1 year's corrected age was evaluated with the Alberta Infant Motor Scale, the Neuro-Sensory Motor Development Assessment, and the diagnosis of cerebral palsy by the child's pediatrician. RESULTS: At 1 year of age, the Alberta Infant Motor Scale categorized 30 (35%) infants as suspicious/abnormal; the Neuro-Sensory Motor Development Assessment categorized 16 (18%) infants with mild-to-severe motor dysfunction, and 5 (6%) infants were classified with cerebral palsy. White matter abnormality at term and general movements at 1 and 3 months significantly correlated with Alberta Infant Motor Scale and Neuro-Sensory Motor Development Assessment scores at 1 year. White matter abnormality and general movements at 3 months were the only assessments that correlated with cerebral palsy. All assessments had 100% sensitivity in predicting cerebral palsy. White matter abnormality demonstrated the greatest accuracy in predicting combined motor outcomes, with excellent levels of specificity (>90%); however, the sensitivity was low. On the other hand, general movements assessments at 1 month had the highest sensitivity (>80%); however, the overall accuracy was relatively low. CONCLUSION: Neuroimaging (MRI) and functional (general movements) examinations have important complementary roles in predicting motor development of very preterm infants.
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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 | 0.010 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".