Imaging selective vulnerability in the developing nervous system
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.002 |
| 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