Bibliographic record
Abstract
OBJECTIVES: The white matter ischemic changes described in the literature are not specific and include white matter rarefaction, axonal damage, myelin degeneration, astrocytic fragmentation and beading of its processes (clasmatodendrosis), oligodendrocyte loss, and microglial activation. This morphological spectrum overlaps with morphological features of many other conditions. This retrospective study aims to describe the cellular changes (using immunohistochemical studies) in a spectrum of ischemic leukoencephalopathy. METHODS: We studied 24 white matter ischemic injury cases with a well-documented interval from the ischemic event of interest. The autopsy reports were reviewed for the clinical information and pathological features to select the most representative areas for other stains and immunostains: luxol fast blue with hematoxylin and eosin (LFB-HE), anti-amyloid precursor protein (APP), anti-glial fibrillary acidic protein (GFAP), and anti-human leukocyte antigen (HLA-DR) antibodies. RESULTS: The early changes detected in mild injury were axonal staining highlighted by APP immunostain and astrocytic and microglial reaction with no significant cellular loss. The most severe injury may lead to losing almost all cellular elements (complete infarct) and replacement by macrophages with time. Injuries in between resulted in a morphological spectrum of selective cellular injury (incomplete infarct), including apoptotic nuclei, axonal staining and swellings, clasmatodendrosis, and loss of ramified microglia. CONCLUSIONS: The pathological findings suggested that widespread axonal staining and swellings are early features followed or accompanied by loss of HLA-DR positive ramified microglia and then by astrocytes in ischemic leukoencephalopathy. The awareness of this morphological spectrum would prevent misdiagnosis, provide a better understanding of this condition and can guide future studies on this important and common subject.
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How this classification was reachedexpand
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) | 0.000 | 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) | 0.003 | 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 teacher head, 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".