Bibliographic record
Abstract
Morphological studies on cerebral ischemia concentrate mainly on the grey matter and white matter changes are regarded as secondary or overlapping injuries. Immunohistochemical (IHC) studies to highlight the combination of various cellular changes in ischemic white matter but have not well documented. We selected 11 archival cases of 3 different ischemic processes (i.e. large vessel occlusion, small vessel occlusion, and hypoperfusion) with survival period range 2-35 days from the ischemic event. The white matter was examined using HE-LFB histochemistry, APP, GFAP, and HLA-DR immunostains focusing on myelin, axonal, astrocytic and microglial changes respectively. The various white matter changes are probably reflective of the different mechanism, duration, severity and extent of ischemia. The APP-IHC shows patchy axonal expression, swelling, and finally complete axonal loss. HLADR-IHC highlights early microglial injuries (fragmentation of processes), complete cell loss, and subsequent replacement by cells of macrophage phenotype. Surrounding the ischemic areas are reactive microglia. Astrocytic changes range from fragmentation of processes (clasmatodendrosis) to different stages of cell loss. Astrocytic swelling tends to occur with cerebral edema. Large vessel occlusion results in complete tissue loss while in small vessel disease the damage is more selective. The injury is generally more subtle in hypoperfusion but can be pronounced focally. Our study has documented the spectrum of white matter injury in different scenarios of cerebral ischemia. Learning Objective Describe the cellular and immunohistochemical changes in the ischemic white matter
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| 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) | 0.003 | 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 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".