White matter injury: Ischemic and nonischemic
Bibliographic record
Abstract
Ischemic pathologies of white matter (WM) include a large proportion of stroke and developmental lesions while multiple sclerosis (MS) is the archetype nonischemic pathology. Growing evidence suggests other important diseases including neurodegenerative and psychiatric disorders also involve a significant WM component. Axonal, oligodendroglial, and astroglial damage proceed via distinct mechanisms in ischemic WM and these mechanisms evolve dramatically with maturation. Axons may pass through four developmental stages where the pattern of membrane protein expression influences how the structure responds to ischemia; WM astrocytes pass through at least two and differ significantly in their ischemia tolerance from grey matter astrocytes; oligodendroglia pass through at least three, with the highly ischemia intolerant pre-oligodendrocyte (pre-Oli) stage linking the less sensitive precursor and mature phenotypes. Neurotransmitters play a central role in WM pathology at all ages. Glutamate excitotoxicity in WM has both necrotic and apoptotic components; the latter mediated by intracellular pathways which differ between receptor types. ATP excitotoxicity may be largely mediated by the P2X7 receptor and also has both necrotic and apoptotic components. Interplay between microglia and other cell types is a critical element in the injury process. A growing appreciation of the significance of WM injury for nonischemic neurological disorders is currently stimulating research into mechanisms; with curious similarities being found with those operating during ischemia. A good example is traumatic brain injury, where axonal pathology can proceed via almost identical pathways to those described during acute ischemia.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".