RCT versus real-world cohorts: Differences in patient characteristics drive associations with outcome after EVT
Bibliographic record
Abstract
Abstract Background: The selection of patients with large-vessel occlusion (LVO) stroke for endovascular treatment (EVT) depends on patient characteristics and procedural metrics. The relation of these variables to functional outcome after EVT has been assessed in numerous datasets from both randomized controlled trials (RCT) and real-world registries, but whether differences in their case mix modulate outcome prediction is unknown. Methods: We leveraged data from individual patients with anterior LVO stroke treated with EVT from completed RCTs from the Virtual International Stroke Trials Archive (N = 479) and from the German Stroke Registry (N = 4079). Cohorts were compared regarding (i) patient characteristics and procedural pre-EVT metrics, (ii) these variables’ relation to functional outcome, and (iii) the performance of derived outcome prediction models. Relation to outcome (functional dependence defined by a modified Rankin Scale score of 3–6 at 90 days) was analyzed by logistic regression models and a machine learning algorithm. Results: Ten out of 11 analyzed baseline variables differed between the RCT and real-world cohort: RCT patients were younger, had higher admission NIHSS scores, and received thrombolysis more often (all p < 0.0001). Largest differences at the level of individual outcome predictors were observed for age (RCT: adjusted odds ratio (aOR), 1.29 (95% CI, 1.10–1.53) vs real-world aOR, 1.65 (95% CI, 1.54–1.78) per 10-year increments, p < 0.001). Treatment with intravenous thrombolysis was not significantly associated with functional outcome in the RCT cohort (aOR, 1.64 (95 % CI, 0.91–3.00)), but in the real-world cohort (aOR, 0.81 (95% CI, 0.69–0.96); p for cohort heterogeneity = 0.056). Outcome prediction was more accurate when constructing and testing the model using real-world data compared to construction with RCT data and testing on real-world data (area under the curve, 0.82 (95% CI, 0.79–0.85) vs 0.79 (95% CI, 0.77–0.80), p = 0.004). Conclusions: RCT and real-world cohorts considerably differ in patient characteristics, individual outcome predictor strength, and overall outcome prediction model performance.
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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 | 0.015 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".