Microembolic Signal Monitoring in Hemispheric Acute Ischaemic Stroke: A Prospective Study
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: There are few data on the occurrence of microembolic signals (MES) in the acute phase of ischaemic stroke. The objective of our work was to systematically study the frequency of MES in non-selected patients with a first-ever hemispheric transient ischemic attack (TIA) or acute cerebral infarction, and to evaluate the clinical usefulness of MES detection. METHODS: 182 consecutive patients with hemispheric TIA or acute cerebral infarction, and 54-age-matched healthy controls were studied. Bilateral transcranial Doppler ultrasound (TCD) monitoring was performed for at least 30 min with a mean time from stroke onset to TCD of 69 h. Stroke severity on admission, early recurrent stroke and dependency on discharge were investigated. RESULTS: MES were detected in 20.5% of patients with arterial sources of embolism, 17. 1% of patients with potential sources of cardioembolism and 5% of patients with cryptogenic stroke. They were not registered, however, in lacunar infarctions (p < 0.001). Stroke severity on admission of patients with MES was greater than that of patients without MES (47. 1 vs. 19.4% with the Canadian Stroke Scale < or =6.5; p = 0.009). Early recurrent stroke was more frequent in patients with MES (11.8%) than in those without MES (4.2%) although the difference was not statistically significant. Multiple logistic regression analysis showed that MES increased the risk of dependency on discharge (odds ratio, 4.2; 95% CI, 1.2-14.9; p = 0.01) independently of age, stroke severity on admission and presence of an arterial or cardiac embolic source. CONCLUSIONS: There is a strong association of MES in the acute phase of stroke with known potential arterial and cardiac embolic sources. MES have an independent predictive value of poor outcome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".