White matter injury: Ischemic and nonischemic
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
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.004 |
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