Transcranial Doppler and Cerebral Augmentation in Acute Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND: Collateral flow augmentation using partial aortic occlusion may improve cerebral perfusion in acute stroke. We assessed the effect of partial aortic occlusion on arterial flow velocities of acute stroke patients. METHODS: Patients with neurological deficits following thrombolysis were treated with partial aortic occlusion. Transcranial Doppler ultrasound (TCD) was used to measure arterial flow velocities at baseline, before and during balloon inflation. The augmented mean flow velocity (MFV), peak systolic velocity (PSV), and end diastolic velocity flow percentages (aMFV%, aPSV%, aEDV%) were calculated and compared based on outcome. RESULTS: Of 11 patients, 3 did not have a temporal window and thus were excluded from our analysis. Six of the remaining 8 patients had middle cerebral artery (MCA) occlusions; the final 2 had terminal internal carotid artery (TICA) occlusions. Three of these 8 patients had good outcome at 90 days (mRS < 3). Before intra-aortic balloon inflation (IABI), the mean affected artery MFV was 23 ± 11 cm/s; during the procedure it was 26 ± 12 cm/s (P = .2). Mean affected artery PSV at baseline and during balloon inflation were 37 ± 16 and 46 ± 23, respectively (P = .1). Mean augmented affected artery MFV% in patients with good long-term outcome was 65.4 ± 46, while the result in those with poor outcome was -3.7 ± 21 (P = .03). Three patients developed anterior cross-filling, and of these 2 had good long-term outcome. CONCLUSION: TCD monitoring of patients treated with IABI may help in predicting outcome in this novel device.
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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 | 0.000 | 0.000 |
| 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".