Stroke Outcome in Clinical Trial Patients Deriving From Different Countries
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Stroke incidence and outcome vary widely within and across geographical locations. We examined whether differences in index stroke severity, stroke risk factors, mortality, and stroke outcome across geographical locations remain after adjusting for case mix. METHODS: We analyzed 3284 patients from the Virtual International Stroke Trials Archive (VISTA). We used logistic regression to examine the incidence of mild index stroke, functional, and neurological outcomes after accounting for age, medical history, year of trial recruitment, and initial stroke severity in the functional and neurological outcome analyses. We examined mortality between geographical regions using a Cox proportional hazards model, accounting for age, initial stroke severity, medical history, and year of trial recruitment. RESULTS: Patients enrolled in the USA and Canada had the most severe index strokes. Those recruited in Austria and Switzerland had the best functional and neurological outcomes at 90 days (P<0.05), whereas those enrolled in Germany had the worst functional outcome at 90 days (P=0.013). Patients enrolled in Austria, Switzerland, Belgium, Netherlands, Finland, Germany, Greece, Israel, Spain, and Portugal had a significantly better survival rate when compared with those enrolled in USA and Canada. Patients enrolled in trials after 1998 had more severe index strokes, with no significant difference in outcome compared with those enrolled before 1998. CONCLUSIONS: We identified regional variations in index stroke severity, outcome, and mortality for patients enrolled in ischemic stroke clinical trials over the past 13 years that were not fully explained by case mix. Index stroke severity was greater in patients enrolled after 1998, with no significant improvement in outcomes compared to those enrolled before 1998.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".