Brain injury in premature neonates: A primary cerebral dysmaturation disorder?
Bibliographic record
Abstract
With advances in neonatal care, preterm neonates are surviving with an evolving constellation of motor and cognitive disabilities that appear to be related to widespread cellular maturational disturbances that target cerebral gray and white matter. Whereas preterm infants were previously at high risk for destructive brain lesions that resulted in cystic white matter injury and secondary cortical and subcortical gray matter degeneration, contemporary cohorts of preterm survivors commonly display less severe injury that does not appear to involve pronounced glial or neuronal loss. Nevertheless, these milder forms of injury are also associated with reduced cerebral growth. Recent human and experimental studies support that impaired cerebral growth is related to disparate responses in gray and white matter. Myelination disturbances in cerebral white matter are related to aberrant regeneration and repair responses to acute death of premyelinating late oligodendrocyte progenitors (preOLs). In response to preOL death, early oligodendrocyte progenitors rapidly proliferate and differentiate, but the regenerated preOLs fail to normally mature to myelinating cells required for white matter growth. Although immature neurons appear to be more resistant to cell death from hypoxia-ischemia than glia, they display widespread disturbances in maturation of their dendritic arbors, which further contribute to impaired cerebral growth. These complex and disparate responses of neurons and preOLs thus result in large numbers of cells that fail to fully mature during a critical window in development of neural circuitry. These recently recognized forms of cerebral gray and white matter dysmaturation raise new diagnostic challenges and suggest new therapeutic directions centered on reversal of the processes that promote dysmaturation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".