Hindbrain regional growth in preterm newborns and its impairment in relation to brain injury
Bibliographic record
Abstract
Premature birth globally affects about 11.1% of all newborns and is a risk factor for neurodevelopmental disability in surviving infants. Histology has suggested that hindbrain subdivisions grow differentially, especially in the third trimester. Prematurity-related brain injuries occurring in this period may selectively affect more rapidly developing areas of hindbrain, thus accompanying region-specific impairments in growth and ultimately neurodevelopmental deficits. The current study aimed to quantify regional growth of the cerebellum and the brainstem in preterm neonates (n = 65 with individually multiple scans). We probed associations of the regional volumes with severity of brain injury. In neonates with no imaging evidence of injury, our analysis using a mixed-effect linear model showed faster growth in the pons and the lateral convexity of anterior/posterior cerebellar lobes. Different patterns of growth impairment were found in relation to early cerebral intraventricular hemorrhage and cerebellar hemorrhage (P < 0.05), likely explaining different mechanisms through which neurogenesis is disrupted. The pattern of cerebellar growth identified in our study agreed excellently with details of cerebellar morphogenesis in perinatal development, which has only been observed in histological data. Our proposed analytic framework may provide predictive imaging biomarkers for neurodevelopmental outcome, enabling early identification and treatment of high-risk patients. Hum Brain Mapp 37:678-688, 2016. © 2015 Wiley Periodicals, Inc.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".