Transcranial Doppler ultrasound in children with stroke and cerebrovascular disorders
Bibliographic record
Abstract
PURPOSE OF REVIEW: A major advantage of transcranial Doppler (TCD) ultrasound is its ability to measure cerebral hemodynamics noninvasively at the patient's bedside. This article summarizes the basic physics and variables used during TCD, recent pediatric data published on the use of TCD in stroke and cerebrovascular disorders and how it may impact diagnosis and management, and some issues to be resolved so that TCD can be put into clinical practice. RECENT FINDINGS: In sickle cell disease in children, TCD is the gold standard stroke prediction tool. Recent data suggest that TCD may provide important information in ischemic stroke because of other childhood arteriopathies such as moyamoya syndrome, transient or focal cerebral arteriopathy, and genetic/syndromic causes. TCD may also detect cerebral emboli and diagnose right-to-left atrial cardiac shunts in children with cryptogenic stroke and transient ischemic attack. SUMMARY: There are many potential clinical applications for TCD in pediatric stroke and cerebrovascular disorders. Additional research in children is needed to determine whether TCD can identify markers of increased stroke risk, elucidate underlying stroke mechanisms, influence the choice of additional testing and treatment, and ultimately impact patient outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".