P.067 Transcranial doppler use in pediatric endovascular thrombectomy post large vessel obstruction secondary to infective endocarditis
Bibliographic record
Abstract
Background: Transcranial doppler ultrasound (TCD) in a pediatric neurocritical setting can determine cerebral hemodynamics by assessing the blood flow velocity in main cerebral arteries. In large vessel occlusions (LVO) that require endovascular thrombectomy (EVT), TCD can monitor recanalization and arterial re-occlusion. We describe one case in a previously healthy 13-year-old girl with a right M1 middle cerebral artery occlusion. Methods: Analysis was done via a retrospective case review. Results: Our patient underwent a successful endovascular thrombectomy (EVT) six hours after symptom onset. Follow up TCDs done at 4, 8, and 24 hours showed stable peak systolic velocities (PSV) on the narrowing of right M1 ranging from 245 to 270 cm/s with stable pre-stenotic PSV around 110 cm/s, indicating focal and stable narrowing of M1 without reocclusion. No high transient signals (HITS) were identified on sub 10 minute TCDs. An urgent echocardiogram revealed a bicuspid aortic valve with vegetations, with later confirmation of infective endocarditis. The patient made an impressive recovery with only mild deficits. Conclusions: TCD can be an effective tool in a pediatric neurocritical setting in guiding initial recanalization after EVT and monitoring for arterial re-occlusion, HITS and hyperperfusion. TCD monitoring also decreases the amount of radiation exposure via CTA.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".