Cerebral Microbleeds: Histopathological Correlation of Neuroimaging
Bibliographic record
Abstract
BACKGROUND: In recent years, there has been a growing interest in cerebral microbleeds (CMBs) and their role in cerebrovascular disease. A few studies have investigated the histopathological correlation between CMBs and neuroimaging findings. We conducted a systematic review in an attempt to characterize the pathological and radiological correlation. METHODS: A systematic literature search was conducted for studies in which CMBs were characterized histopathologically and correlated with MRI findings. RESULTS: Five studies met the inclusion criteria, with a total of 18 patients. Hemosiderin deposition was reported in 42 CMBs (49%), while 16 CMBs (19%) were described as old hematomas which stained for iron, 13 (15%) had no associated specific pathology, 11 (13%) contained intact erythrocytes, 1 (1%) was due to vascular pseudocalcification, 1 (1%) was a microaneurysm and 1 (1%) was a distended dissected vessel. Lipofibrohyalinosis was the most prominent associated vascular finding. Amyloid angiopathy was present primarily in patients with dementia. CONCLUSIONS: Although histopathological associations have been observed using MRI in patients with CMBs, the findings have yet to be validated and further research is warranted.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".