Predicting Motor Development in Very Preterm Infants at 12 Months’ Corrected Age: The Role of Qualitative Magnetic Resonance Imaging and General Movements Assessments
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it