Diagnosis and Management of Acute Ischemic Stroke
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it