Bibliographic record
Abstract
Background: White matter changes are common finding during brain autopsies especially in elderly. Although there are many studies applying radiological-pathological correlation on these lesions, their pathogenesis is still unclear. However, a number of possible causes have been suggested including: hypoxic-ischemia, altered blood brain barrier permeability, vascular pathology and chronic hypoperfusion. As usually there is a multiplicity of causes in any individual case, it is very difficult to pinpoint the major causal factor contributing to observed pathological changes. In this study, we document the white matter pathology in global acute hypoxic/ischemic injury due to cardiac arrest as the major causal factor. Method: We retrieve 16 cases of cardiac arrest encephalopathy in our archive with post arrest survival range from 6 hours to 14 days. Several special, and immunohistochemical stains were used to study the axonal and myelin pathology. Result: The pathogenicity of the cardiac arrest was confirmed in all cases by demonstrating the expected gray matter pathology, albeit in varying degree of severity. The white matter changes range from unremarkable in the first 2 days, evidence of cerebral edema (visualized from 3rd day on), and early axonal degeneration, to diffuse myelin pallor secondary to marked axonal loss on day 14. Conclusion: The white matter changes in post cardiac arrest are mainly due to early cerebral edema and axonal degeneration and the effect on myelin is secondary.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.001 |
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".