Perioperative strokes: uncovering risks, sequelae, and a therapeutic future
Bibliographic record
Abstract
Abstract This article provides an overview of perioperative strokes—a pressing concern given the rising number of surgical or interventional procedures performed worldwide. Mechanisms underlying perioperative stroke include atherosclerotic plaque instability, induction of a pro-inflammatory state (aggravated by vascular risk factors), hemodynamic dysfunction through hypotension and blood loss, and disruption of the endothelial glycocalyx. The frequency of perioperative stroke varies considerably depending on the type of procedure, being higher with aortic valve and neurovascular procedures. Covert or silent strokes are commonly seen on post-operative magnetic resonance imaging in as many as one in two patients after procedures like brain aneurysm coiling. Risk factors for perioperative stroke include patient factors such as age, sex, race, and comorbidities, as well as operator and procedural factors such as operator experience, institutional procedural volume, use of certain devices, and vascular access site. Overt periprocedural stroke is associated with higher mortality, longer hospital stays, and higher long-term disability. The long-term sequelae of covert strokes are still being characterized, but recent studies have indicated that a higher burden of such infarcts is associated with worse functional and cognitive outcomes. Key considerations to prevent perioperative strokes include screening plus risk factor control, pre-medication, and procedural considerations including anesthetic choice. The management of perioperative ischemic stroke has been aided by advancements in reperfusion therapies and stroke systems of care that allow rapid treatment of major stroke. Ongoing work seeks to address the enduring need for evidence-based therapeutic strategies to prevent these strokes and mitigate their adverse impact.
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.
How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".