The development and preliminary validation of a Preference-Based Stroke Index (PBSI).
Bibliographic record
Abstract
BACKGROUND: Health-related quality of life (HRQL) is a key issue in disabling conditions like stroke. Unfortunately, HRQL is often difficult to quantify in a comprehensive measure that can be used in cost analyses. Preference-based HRQL measures meet this challenge. To date, there are no existing preference-based HRQL measure for stroke that could be used as an outcome in clinical and economic studies of stroke. The aim of this study was to develop the first stroke-specific health index, the Preference-based Stroke Index (PBSI). METHODS: The PBSI includes 10 items; walking, climbing stairs, physical activities/sports, recreational activities, work, driving, speech, memory, coping and self-esteem. Each item has a 3-point response scale. Items known to be impacted by a stroke were selected. Scaling properties and preference-weights obtained from individuals with stroke and their caregivers were used to develop a cumulative score. RESULTS: Compared to the EQ-5D, the PBSI showed no ceiling effect in a high-functioning stroke population. Moderately high correlations were found between the physical function (r = 0.78), vitality (r = 0.67), social functioning (r = 0.64) scales of the SF-36 and the PBSI. The lowest correlation was with the role emotional scale of the SF-36 (r = 0.32). Our results indicated that the PBSI can differentiate patients by severity of stroke (p < 0.05) and level of functional independence (p < 0.0001). CONCLUSIONS: Content validity and preliminary evidence of construct validity has been demonstrated. Further work is needed to develop a multiattribute utility function to gather information on psychometric properties of the PBSI.
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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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".