Longitudinal cerebellar growth following very preterm birth
Bibliographic record
Abstract
PURPOSE: To measure cerebellar growth in a longitudinal cohort of very preterm infants to identify early predictors of subsequent brain growth. Although the cerebellum grows rapidly during late gestation, the rate and variability of growth following premature birth, and the effects of associated injury, are largely unknown. MATERIALS AND METHODS: In all, 105 very-preterm born infants (24-32 weeks GA) were imaged using magnetic resonance imaging (MRI) at birth, term-equivalent, 2, and 4 years of age. Cerebellar and total cerebral volumes were estimated from 1 mm isotropic T1 -weighted scans acquired at 1.5T and 3T, using an atlas-based approach. Linear models were used to analyze cerebellar volume as cross-sectional and longitudinal functions of age, clinical, and radiological correlates. Linear models were also used to test for associations between volume and cognitive outcome. RESULTS: Cerebellar volume increased rapidly with age-at-scan during both the preterm (0.7 mL/wk, P < 0.001) and term periods (1.8 mL/wk, P < 0.001). Infants with grade 3 or 4 germinal matrix hemorrhage (GMH) had smaller cerebellar volumes as a percentage of total brain volume starting at birth and continuing to 4 years of age (-0.43%, -0.57%, -1.09% at preterm, term, and 4 years, respectively, P < 0.01). Irrespective of age-at-scan, early cerebellar volume was predictive of volume at 4 years of age (slope = 1.3, P < 0.001). Cerebellar volumes were not found to predict cognitive outcome at 4 years of age; P < 0.2. CONCLUSION: High-grade GMH and small perinatal cerebellar size is predictive of cerebellar development up to 4 years of age. These findings suggest that it is possible to identify individuals at high risk of reduced cerebellar volumes at an early age. J. Magn. Reson. Imaging 2016;43:1462-1473.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".