Intracranial microembolic signals might be a potential risk factor for cognitive impairment
Bibliographic record
Abstract
Objective: We aimed to explore the relationship between microembolic signals (MES) and cognitive impairment in patients with neurological disorders using a 30-minute MES monitoring test.Methods: We retrospectively reviewed patients who visited outpatient departments and underwent a 30-minute MES monitoring session using dual-channel transcranial doppler (TCD) at Beijing Tiantan hospital between July 2016 and December 2018. All patients completed the Montreal Cognitive Assessment (MoCA) and underwent magnetic resonance imaging (MRI). Cognitive impairment was defined as a MoCA score of less than 26. MES were identified according to the criteria of the International Consensus Group on Microembolus Detection.Results: Of the 1356 subjects who underwent MES monitoring, 159 patients (including 50 cases of MES positive and 109 cases of MES negative) had both analyzable MES monitoring recording and cognition evaluation data, of which 72 had cognitive impairment. Compared with the group with no deficits in cognitive function, the proportion of MES positive was significantly higher in patients with impaired cognitive function – that is, 47% (34/72) versus 18.4% (16/87), respectively, with p < 0.05. In multivariate logistic regression analysis, MES were independently associated with lower MoCA score (odd ratios (OR), 7.36; 95% confidence intervals (CI), 2.72–19.85, P < 0.0001).Conclusions: In this retrospective study, we found a possible correlation and relationship between MES and cognitive impairment. Further studies are required to determine whether continuous cerebral microembolization to the brain will lead to progressive cognitive impairment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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".