Transcranial Doppler to evaluate postreperfusion therapy following acute ischemic stroke: A literature review
Bibliographic record
Abstract
Cerebral vessel recanalization therapy, either intravenous thrombolysis or mechanical thrombectomy, is the main treatment that can significantly improve clinical outcomes after acute ischemic stroke. The degree of recanalization and cerebral reperfusion of the ischemic penumbra are dependent on cerebral hemodynamics. Currently, the main imaging modalities to assess reperfusion are MRI and CT perfusion. However, these imaging techniques cannot predict reperfusion-associated complications and are not readily available in many centers. It is also not feasible to repeat them frequently for sequential assessments, which is important because of the changing nature of cerebral hemodynamics following stroke. Transcranial Doppler sonography (TCD) is a valid, safe, and inexpensive technique that can assess recanalized vessels and reperfused tissue in real-time at the bedside. Post thrombectomy reocclusion, hyperperfusion syndrome, distal embolization, and remote infarction result in poor outcomes after mechanical or intravenous reperfusion therapy. Managing blood pressure following these endovascular treatments can also be a dilemma. TCD has an important role, with major clinical implications, in evaluating cerebral hemodynamics and collateral vessel status, guiding clinicians in making individualized decisions based on cerebral blood flow during acute stroke care. This review summarizes the most relevant literature on the role of TCD in evaluating patients after reperfusion therapy. We also discuss the importance of performing TCD in the first few hours following thrombolytic therapy in identifying hyperperfusion syndrome and embolic signals, predicting recurrent stroke, and detecting reocclusions, all of which may help improve patient prognosis. We recommend TCD during the hyperacute phase of stroke in comprehensive stroke centers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".