Selective intra-arterial hypothermia combined with endovascular thrombectomy for large vessel occlusion: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Systemic therapeutic hypothermia may improve outcomes after acute ischemic stroke but increases complications. Selective intra-arterial hypothermia at the ischemic site during endovascular thrombectomy (EVT) theoretically offers benefits with fewer risks. However, there is little clinical evidence to support this approach. METHODS: We searched Medline/PubMed, Embase and Cochrane electronic databases for studies evaluating the safety and feasibility of selective intra-arterial hypothermia as an adjunct to EVT for large vessel occlusion (LVO). Effect sizes with 95% confidence intervals (CIs) were pooled using the fixed-effect model. Odds ratios (ORs) were computed for binary variables, while the mean differences (MDs) were pooled for continuous data. RESULTS: Of identified records, five clinical studies involving 463 LVO patients (62.9% male) were included. Of those, 224 (48.4%) patients received adjuvant selective intra-arterial hypothermia, while 239 (51.6%) received EVT alone. Selective intra-arterial hypothermia resulted in higher rates of good functional outcome (modified Rankin scale [mRS] 0-2 at 90-days) (OR 2.07, [95% CI, 1.36 to 3.16]), and lower final infarct volume (MD, -20.96 ml [95% CI, -26.17 to -15.75]) and lower rates of severe disability (mRS 3-5 at 90 days) (OR 0.44 [95% CI, 0.26 to 0.75]). Safety parameters including rates of symptomatic intracerebral hemorrhage, mortality, pneumonia, coagulation abnormalities, and arterial spasm were comparable between groups. CONCLUSIONS: The initial evidence supports the safety and feasibility of selective intra-arterial hypothermia when combined with EVT for LVO. This approach shows promise for advancing research on neuroprotective strategies for ischemic stroke.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".