How Well Do Standard Stroke Outcome Measures Reflect Quality of Life?
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Quality of life (QoL) is important to stroke survivors yet is often recorded as a secondary measure in acute stroke randomized controlled trials. We examined whether commonly used stroke outcome measures captured aspects of QoL. METHODS: We examined primary outcomes by National Institutes of Health Stroke Scale (NIHSS), Barthel Index (BI) and modified Rankin Scale (mRS), and QoL by Stroke Impact Scale (SIS) and European Quality of Life Scale (EQ-5D) from the Virtual International Stroke Trials Archive (VISTA). Using Spearman correlations and logistic regression, we described the relationships between QoL mRS, NIHSS, and BI at 3 months, stratified by respondent (patient or proxy). Using χ2 analyses, we examined the mismatch between good primary outcome (mRS ≤1, NIHSS ≤5, or BI ≥95) but poor QoL, and poor primary outcome (mRS ≥3, NIHSS ≥20, or BI ≤60) but good QoL. RESULTS: Patient-assessed QoL had a stronger association with mRS (EQ-5D weighted score n=2987, P<0.0001, r=-0.7, r2=0.53; SIS recovery n=2970, P<0.0001, r=-0.71, r2=0.52). Proxy responses had a stronger association with BI (EQ-5D weighted score n=837, P<0.0001, r=0.78, r2=0.63; SIS recovery n=867, P<0.0001, r=0.68, r2=0.48). mRS explained more of the variation in QoL (EQ-5D weighted score=53%, recovery by SIS v3.0=52%) than NIHSS or BI and resulted in fewer mismatches between good primary outcome and poor QoL (P<0.0001, EQ-5D weighted score=8.5%; SIS recovery=10%; SIS-16=4.4%). CONCLUSIONS: The mRS seemed to align closely with stroke survivors' interests, capturing more information on QoL than either NIHSS or BI. This further supports its recommendation as a primary outcome measure in acute stroke randomized controlled trials.
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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 | 0.090 | 0.217 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".