Abstract TP186: Heterogeneity Of Endovascular Treatment Effect: A Comparison Of Subgroup Identification Methods In Acute Stroke Trials
Bibliographic record
Abstract
Background: Trials’ data are increasingly re-analyzed to identify treatment effect heterogeneity: that is, subgroups of patients who have either enhanced or adverse effects in a trial. This study investigates the robustness of subgroup identification methods in an acute stroke trial. Methods and Analysis: The Model-based recursive partitioning (MOB), Stochastic Subgroup Identification based on Differential Effects Search (Stochastic SIDEScreen), and Virtual Twin (VT) methods would be used to detect heterogeneity in Endovascular Treatment for Small Core and Anterior Circulation Proximal Occlusion with Emphasis on Minimizing CT to Recanalization Times (ESCAPE) trial. Results: In the ESCAPE trial, patients in the intervention group had a higher rate of functional independence (90-day mRS 0-2) than those in the control group (OR=2.6; p<0.001, and 95% CI=1.7–3.8). The three methods identified patients with differential treatment effects. The MOB identified 2-terminal subgroups, with the NIHSS > 11 group showing a significant treatment effect (OR=3.67; p<0.001 and 95% CI=2.11–6.40), while the subgroup of with a maximum NIHSS score of 11 did not (OR=1.63; p=0.463 and 95% CI=0.44–6.05). The stochastic SIDEScreen identified 4-terminal subgroups, but the group of patients with NIHSS greater than 9 and older than 54 years had a significant treatment effect (OR=4.92; p<0.001, and 95% CI= 2.66–9.10). Other three subgroups, like patients with a maximum NIHSS score of 9 and older than 54 years (OR=2.17, p=0.34, and 95% CI=0.44–10.65), did not have a significant treatment effect. VT identified 6-terminal subgroups; the subgroup consisting of patients older than 56 years and NIHSS > 11 had significant treat effect (OR=5.11; p<0.001 and 95% CI=2.68–9.73). As other renaming 4 subgroups, the subgroup consisting of younger patients and with a maximum NIHSS score of 11 did not show a treatment effect (OR=1.60, p=0.64, and 95% CI=0.39–6.30). Conclusion: Data-driven subgroup identification methods provide insight into the heterogeneity of treatment effects in acute stroke trials. Information about the identified subgroups might inform the development of clinical practice guidelines for acute stroke management.
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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 | 0.117 | 0.255 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.015 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".