Endovascular interventions for acute stroke: past practice and current research
Bibliographic record
Abstract
The way clinical care and research are currently conceived is entangled in multiple problems. While the problems are universal, nowhere were they more painfully experienced than in the acute care of patients with severe stroke. While we are celebrating that we now possess evidence that patients can benefit from endovascular interventions, we should not miss the opportunity to review the difficult path that led to this point and reflect on how it could be improved. A narrative review of the past and current status of care and research in endovascular treatment of acute stroke may serve to expose the problems and potential solutions. For more than 25 years, rescue endovascular interventions were offered to selected patients using a vast array of drugs and devices not designed for that purpose with little success, at least in the early years.1 Occasionally, a Lazarus experience (spectacular recovery associated with recanalization) would fuel enough enthusiasm to go on for another long run of failures. We have always been aware that the occasional miracle could not justify a full-scale campaign for the massive resources required to staff centers with expert personnel, streamlined patient transportation, not to mention the generous budget needed for such high-technology care. Clinical research thus focused on intravenous (IV) therapy, a simpler approach which can be delivered locally.2 Following the positive National Institute of Neurological Disorders and Stroke (NINDS) study, subsequent research endeavors were largely devoted to neuroprotective agents promoted by the pharmaceutical industry. Meanwhile, increasingly effective endovascular tools became available, but regulatory agencies never required proof of clinical benefit (angiographic demonstration that vessels can be recanalized would typically suffice) to authorize the sale of ever more expensive devices. Intra-arterial (IA) therapy thus did not have the financial impetus to rigorously assess patient outcomes. Until recently, in the presence of large …
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".