Acute ischemic stroke management: concepts and controversies.A narrative review
Bibliographic record
Abstract
INTRODUCTION: Amongst the 25.7 million survivors and 6.5 million deaths from stroke between 1990 and 2013, ischemic strokes accounted for approximately 70% and 50% of the cases, respectively. With patients still suffering from complications and stroke recurrence, more questions have been raised as to how we can better improve patient management. AREAS COVERED: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement and Newcastle-Ottawa Scale (NOS) were adopted to ensure a comprehensive inclusion of quality literature from various sources. PubMed and Embase were searched for evidence on thrombolysis, mechanical thrombectomy, artificial intelligence (AI), antiplatelet therapy, anticoagulation and hypertension management. EXPERT OPINION: The directions of future research in these areas are dependent on the current level of validation. Endovascular therapy and applications of AI are relatively new compared to the other areas discussed in this review. As such, future studies need to focus on validating their efficacy. As for thrombolysis, antiplatelet and anticoagulation therapy, their efficacy has been well-established and future research efforts should be directed toward adjusting its use according to patient-specific factors, starting with factors with the most clinical relevance and prevalence.
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 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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".