Abstract WP13: Derivation and Validation of Utility Weights for the Modified Rankin Scale From the AcT Thrombolysis Trial
Bibliographic record
Abstract
Introduction: Utility-weighted mRS (UW-mRS) is increasingly being adopted as an important clinical endpoint in stroke trials as it offers some gains in statistical efficiency and power when estimating treatment effects. The widely used utility weights for estimating UW-mRS, derived from a systematic review of acute stroke trials, are prone to substantial variability across and between mRS categories. This study aims to derive utility weights for UW-mRS by directly mapping 5-item EuroQoL (EQ-5D-5L) responses to mRS scores. Methods: Data were collected from all patients included in Alteplase compared to Tenecteplase (AcT) trial. Quality of life at 90 days post-randomization was assessed using EQ-5D-5L. Health utilities (EQ-5D index) were estimated using the time trade-off approach based on Canadian norms and imputed as zero for patients who died. Using a predictive linear model, utility weights were derived by regressing the ordinal mRS on the utilities. Model performance was measured using R 2 and root mean square error (RMSE) after 5-fold cross-validation. We compared the distribution of the UW-mRS scores using the systematic review and model-based weights. Results: Of the 1503 acute stroke patients who completed the EQ-5D-5L questionnaire at 90 days, 717 (47.7%) were female, and the median (interquartile range [IQR]) age was 74.0 (20.0). The median (IQR) estimated health utility was 0.81 (0.52). The utility weights for mRS categories 0-6 were 0.93, 0.90, 0.80, 0.67, 0.41, 0.24, and 0.00. The model performed well after 5-fold cross-validation (R 2 = 0.89; RMSE = 0.12). The model-based weights had higher mean and median UW-mRS scores but smaller variability across mRS levels than those from the systematic review-based weights. There was no statistically significant difference in the median UW-mRS scores across both treatment groups for each utility weight type. Conclusion: The estimation of UW-mRS scores largely depends on the choice of weights used, which depends on sample heterogeneity in treatment interventions and stroke severity of the cohorts. Model-based approaches result in reduced variability between and across mRS levels. Future research will seek to validate these utility weights externally in an independent cohort of patients with stroke.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.224 | 0.478 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".