Optimizing First-Pass Complete Reperfusion in Acute Ischemic Stroke: Pearls and Pitfalls
Bibliographic record
Abstract
Acute ischemic stroke (AIS), particularly if caused by a large vessel occlusion (LVO), is a severely disabling, life-threatening disease. In 2015, five major randomized controlled trials have shown the benefit of endovascular treatment (EVT) compared with intravenous alteplase in AIS patients with LVO,[ 1 ] and since then, EVT is considered standard of care. EVT significantly reduces disability in LVO patients and the number needed to treat for reduction of disability by at least one point on the modified Rankin Scale is 2.6.[ 1 ] The safety profile of EVT is excellent, with no significant differences in mortality and symptomatic intracranial hemorrhage compared with intravenous alteplase treatment alone.[ 1 ] Given this powerful treatment option and the low recanalization rates of LVOs with tissue plasminogen activator alone, many physicians, including ourselves, now offer EVT routinely beyond guideline recommendations. On average, every 30-minute delay in recanalization decreases the chance of a good functional outcome by 8 to 14%.[ 2 ] Thus, reperfusion has to be achieved fast. Reperfusion quality (i.e., how well we open a vessel) is another key determinant of patient outcome: higher expanded treatment in cerebral infarction (eTICI) grades are strongly associated with good patient outcome.[ 3 ] The eTICI score reflects the final reperfusion result, but complete recanalization sometimes requires multiple device passes,[ 4 ] which yields an increased risk of endothelial injury. First-pass effect (i.e., achieving complete revascularization with a single device pass) is an independent predictor for good outcome. Fast and complete reperfusion is also beneficial from an economic standpoint: In the United States, the net monetary benefit per patient is on average $17,000 per 1% increase in the final eTICI IIc/III rate and $10,600 per 10 minutes of time-to-treatment decrease.[ 5 ] [ 6 ]
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.006 | 0.018 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".