Visual aid tool to improve decision making in acute stroke care
Bibliographic record
Abstract
Background Acute stroke care represents a challenge for decision makers. Recent randomized trials showed the benefits of endovascular therapy. Our goal was to provide a visual aid tool to guide clinicians in the decision process of endovascular intervention in patients with acute ischemic stroke. Methods We created visual plots (Cates' plots; www.nntonline.net ) representing benefits of standard of care vs. endovascular thrombectomy from the pooled analysis of five RCTs using stent retrievers. These plots represent the following clinically relevant outcomes (1) functionally independent state (modified Rankin scale (mRS) 0 to 2 at 90 days) (2) excellent recovery (mRS 0-1) at 90 days, (3) NIHSS 0-2 (4) early neurological recovery, and (5) revascularization at 24 h. Subgroups visually represented include time to treatment and baseline stroke severity strata. Results Overall, 1287 patients (634 assigned to endovascular thrombectomy, 653 assigned to control were included to create the visual plots. Cates' visual plots revealed that for every 100 patients with acute ischemic stroke and large vessel occlusion, 27 would achieve independence at 90 days (mRS 0-2) in the control group compared to 49 (95% CI 43-56) in the intervention group. Similarly, 21 patients would achieve early neurological recovery at 24 h compared to 54 (95% CI 45-63) out of 100 for the intervention group. Conclusion Cates' plots may assist clinicians and patients to visualize and compare potential outcomes after an acute ischemic stroke. Our results suggest that for every 100 treated individuals with an acute ischemic stroke and a large vessel occlusion, endovascular thrombectomy would provide 22 additional patients reaching independency at three months and 33 more patients achieving ENR compared to controls.
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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 | 0.032 | 0.211 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.063 | 0.007 |
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".