Visual Aids for Patient, Family, and Physician Decision Making About Endovascular Thrombectomy for Acute Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Rapid decision making optimizes outcomes from endovascular thrombectomy for acute cerebral ischemia. Visual displays facilitate swift review of potential outcomes and can accelerate decision processes. METHODS: From patient-level, pooled randomized trial data, 100 person-icon arrays (Kuiper-Marshall personographs) were generated showing beneficial and adverse effects of endovascular thrombectomy for patients with acute cerebral ischemia and large vessel occlusion using (1) automated (algorithmic) and (2) expert-guided joint outcome table specification. RESULTS: For the full 7-category modified Rankin Scale, thrombectomy added to IV tPA (intravenous tissue-type plasminogen activator) alone had number needed to treat to benefit 2.9 (95% confidence interval, 2.6-3.3) and number needed to harm 68.9 (95% confidence interval, 40-250); thrombectomy for patients ineligible for IV tPA had number needed to treat to benefit 2.3 (95% confidence interval, 2.1-2.5) and number needed to harm 100 (95% confidence interval, 62.5-250). Visual displays of treatment effects on 100 patients showed: with thrombectomy added to IV tPA alone, 34 patients have better disability outcome, including 14 more normal or near normal (modified Rankin Scale, 0-1); with thrombectomy for patients ineligible for IV tPA, 44 patients have a better disability outcome, including 16 more normal or nearly normal. Displays also showed that harm (increased modified Rankin Scale final disability) occurred in 1 of 100 patients in both populations, mediated by increased new territory infarcts. The person-icon figures integrated these outcomes, and early side-effects, in a single display. CONCLUSIONS: Visual decision aids are now available to rapidly educate healthcare providers, patients, and families about benefits and risks of endovascular thrombectomy, both when added to IV tPA in tPA-eligible patients and as the sole reperfusion treatment in tPA-ineligible patients.
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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 | 0.008 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.054 | 0.005 |
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".