Imaging selective vulnerability in the developing nervous system
Bibliographic record
Abstract
Why do cells in the central nervous system respond differently to different stressors and why is this response so age-dependent? In the immature brain, there are regions of selective vulnerability that are predictable and depend on the age when the insult occurs and the severity of the insult. This damage is both region and cell population specific. Vulnerable cell populations include the subplate neurons and oligodendrocyte precursors early in development and the neurons closer to the end of human gestation. Mechanisms of injury include excitotoxicity, oxidative stress and inflammation as well as accelerated apoptosis. Advanced imaging techniques have shown us particular patterns of injury according to age at insult. These changes seen in the newborn at the time of injury on magnetic resonance imaging correlate well with the neurodevelopmental outcome. New questions about how the injury evolves and how the newborn brain adapts and repairs itself have emerged as we now know that injury in the newborn brain can evolve over days and weeks, rather than hours. The ability to follow these processes has allowed us to investigate the role of repair in attenuating the injury. Neurogenesis and angiogenesis exist in response to ischemic injury and can be enhanced by processes that are known to protect the brain. The injury response in the developing brain is a complex process that evolves over time and is amenable to repair.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".