Neuroradiological markers of Vascular Cognitive Impairment after Stroke
Bibliographic record
Abstract
Background: Cognitive vascular impairment (CCV) is a frequent, but overlooked, possible consequence of stroke. Neuroimaging is essential for the evaluation of these patients with cognitive deficits supposedly secondary to vascular lesions, with Nuclear Magnetic Resonance (NMR) of the skull being the most sensitive method for identifying markers associated with CCV. The most relevant markers seem to be, among others, strategic location, severity of white matter changes, as well as the degree of atrophy of the medial temporal lobe. Objective: To assess the relationship between stroke and CCV using markers from skull MRI. Methodology: This is a systematic review of observational studies published between 2005 and 2020. The search was carried out in the PubMed and SciELO databases with the keywords consulted by the following MeSH and DeCS sites: “stroke”, “MRI”, “Vascular cognitive impairment”, using the boolean operator “and”. The PRISMA check-list was used to guide this review. Results: According to the eligibility criteria, eight studies were selected. “Event location” was the marker in MRI of the skull most frequently considered, being the object of evaluation in seven of the eight studies analyzed and proving to be a statistically significant marker (p <0.05) for the prediction of CCV in six of them. 75% of the studies included in this review evaluated the relationship between the presence of “hyperintensity in the white matter” at MRI and CCV. However, this marker was shown to be statistically significant in 50% of these studies. Conclusion: A review that brought together the assessment of a wide range of possible neuroradiological predictors of CVD after stroke had not been carried out so far. It would be particularly useful to evaluate the markers in a more homogeneous way in a study with a larger sample size, which would allow quantitative analysis to measure the influence of each predictor.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".