Thrombolysis in Stroke Mimics: Comprehensive Stroke Centers vs Telestroke Sites
Bibliographic record
Abstract
ABSTRACT: Background: Hyperacute treatment of acute stroke may lead to thrombolysis in stroke mimics (SM). Our aim was to determine the frequency of thrombolysis in SM in primary stroke centers (PSC) dependent on telestroke versus comprehensive stroke centers (CSC). Method: Retrospective review of prospectively collected data from the Quality improvement and Clinical Research (QuICR) registry, the Discharge Abstract Database (DAD), and The National Ambulatory Care Reporting System (NACRS) of consecutive patients treated with intravenous thrombolysis for acute ischemic stroke in Alberta (Canada) from April 2016 to March 2021. Result: A total of 2471 patients who received thrombolysis were included. Linking the QuICR registry to DAD 169 (6.83%) patients were identified as SM; however, on our review of the records, only 112 (4.53%) were actual SM. SMs were younger with a mean age of 61.66 (±16.15) vs 71.08 (±14.55) in stroke. National Institute of Health Stroke Scale was higher in stroke with a median (IQR) of 10 (5–17) vs 7 (5–10) in SM. Only one patient (0.89 %) in SM groups had a small parenchymal hemorrhage versus 155 (6.57%) stroke patients had a parenchymal hemorrhage. There was no death among patients of thrombolysed SM during hospitalization versus 276 (11.69%) in stroke. There was no significant difference in the rate of SM among thrombolysed patients between PSC 27 (5.36%) versus CSC 85 (4.3%) (P = 0.312). The most responsible diagnosis of SM was migraine/migraine equivalent, functional disorder, seizure, and delirium. Conclusion: The diagnosis of SM may not always be correct when the information is extracted from databases. The rate of thrombolysis in SM via telestroke is similar to treatment in person at CSC.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".