Correlations in post‐mortem imaging‐histopathology studies of sporadic human cerebral small vessel disease: A systematic review
Bibliographic record
Abstract
AIMS: Sporadic human cerebral small vessel disease (SVD) commonly causes stroke and dementia but its pathogenesis is poorly understood. There are recognised neuroimaging and histopathological features. However, relatively few studies have examined the relationship between the radiological and pathological correlates of SVD; better correlation would promote greater insight into the underlying biological changes. METHODS: We performed a systematic review to collate and appraise the information derived from studies that correlated histological with neuroimaging-defined SVD lesions. We searched for studies describing post-mortem imaging and histological tissue examination in adults, extracted data from published studies, categorised the information and compiled this narrative. RESULTS: We identified 38 relevant studies, including at least 1146 subjects, 342 of these with SVD: 29 studies focussed on neuroradiological white matter lesions (WML), six on microinfarcts and three on dilated perivascular spaces (PVS) and lacunes. The histopathology terminology was diverse with few robust definitions. Reporting and methodology varied widely between studies, precluding formal meta-analysis. PVS and 'oedema' were frequent findings in WML, being described in at least 94 and 18 radiological WML, respectively, in addition to myelin pallor. Histopathological changes extended beyond the radiological lesion margins in at least 33 radiological WML. At least 43 radiological lesions not seen pathologically and at least 178 histological lesions were not identified on imaging. CONCLUSIONS: Histopathological assessment of human SVD is hindered by inconsistent methodological approaches and unstandardised definitions. The data from this systematic review will help to develop standardised definitions to promote consistency in human SVD research.
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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 | 0.014 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".