Role of susceptibility-weighted imaging in detection of cerebral microbleeds and its relation with cognitive impairment
Bibliographic record
Abstract
Objective To study the cerebral microbleeds(CMBs) on susceptibility-weighted imaging (SWI) and analyze the correlation between CMBs at different sites and cognitive impairment. Methods CMBs was detected in 189 patients on SWI.The patients were divided into positive CMBs group(n = 72) and negative CMBs group(n = 74).Their cognitive function was assessed according to the Montreal Cognitive Assessment(MoCA) Scale and the Mini-Mental State Examination (MMSE) Scale,respectively.Cognitive impairments due to CMBs at different sites in two groups were compared.Results The number of CMBs lesions on SWI was significantly greater than that on MRI.The total score and the scores of visual space and executive function,nomenclature, memory,attention power,linguistics,ability and orientation force of CMBs patients as shown on the MoCA and MMSE Scales were significantly lower in CMBs group than in control group(P0.01).CMBs was usually located in the basal ganglia and temporal lobe.The total score of cognitive function was higher on the MoCA Scale than on the MMSE Scale(P0.01).CMBs was found to be related with cognitive impairment(r= -0.51,P0.01).Conclusion SWI is more sensitive than MRI to CMBs.CMBs sites are closely related with cognitive impairment in CMBs patients.MoCA Scale is more sensitive than MMSE Scale to cognitive function in CMBs patients.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".