Diagnosis and Management of Acute Ischemic Stroke
Bibliographic record
Abstract
This chapter will review updates in the various imaging modalities used to diagnose acute ischemic stroke (AIS), how these are used to select patients for intervention, and the different interventions used for management of AIS. The backbone of the AIS diagnostic algorithm remains the computed tomography scan (CT) given its speed of use and sensitivity. CT-angiography (CTA) is crucial in diagnosing large-vessel occlusions (LVOs) and multiphase CTA and CT-perfusion (CTP) can demonstrate the number of collaterals in the area and remaining salvageable tissue. MRI can be used to select patients presenting in an unknown time window for thrombolysis. The primary goal of AIS management is to rescue the ischemic penumbra and the approach to treating AIS has gone from a time-based to tissue-based approach. While tPA is still the agent of choice for thrombolysis in patients with AIS, tenecteplase (TNK) may be just as effective and more efficient to use. Endovascular thrombectomy (EVT) has shown considerable efficacy for alleviating LVOs and using CTP, patients can be selected for hours after symptom-onset if viable tissue remains. It remains unclear if an “EVT-alone” strategy is superior to “tPA + EVT” strategy but this may be dependent on clot, patient, and geographical characteristics.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.017 |
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".