Too risky, too large, too late, or too mild—Reasons for not treating ischemic stroke patients and the related outcomes
Bibliographic record
Abstract
Background: Despite effective treatments, many patients are still not offered reperfusion therapy for acute ischemic stroke. Methods: We present a single-center observational study on acute ischemic stroke patients, who presented as candidates for reperfusion therapy but were deemed ineligible after work-up. Reasons for non-treatment were obtained by studying patient files and subsequently grouped into "too risky" (e.g., anticoagulant use, comorbidities), "too large" (large infarct), "too late" (late presentation of stroke and wake-up strokes), or "too mild" (clinically mild/remitting symptoms). Modified Rankin scale (mRS) score was prospectively collected in all patients by a structured telephone interview. All non-treated patients with a National Institute of Health Stroke Scale (NIHSS) score of 0-5 were compared with a similar cohort that was treated. Results: Of 529 patients with acute ischemic stroke arriving as reperfusion therapy candidates, 198 (37.4%) were not treated. The majority (42%) were not treated due to admission outside the treatment window (too late) and 24% had absolute contraindications (too risky). Only 8% was excluded because their infarct was too large [median Alberta Stroke Program Early CT score 3 (2-4)]. In the "too mild" group (14%) the percentage of patients not being independent at 90 days was 30%. The adjusted odds ratio for a better outcome (lower mRS) among treated patients with NIHSS 0-5 compared with non-treated was 1.93 (95% confidence interval 1.15-3.23). Conclusion: Presenting outside the treatment window is still the most common reason for not receiving therapy. Our study suggests a benefit of thrombolysis for patients with mild symptoms.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".