Assessment of disease impact through health‐related quality of life measurement in primary progressive aphasia
Bibliographic record
Abstract
INTRODUCTION: Measurements of health-related quality of life (HRQoL) are important for capturing disease impact beyond physical health and relative to other diseases but have rarely been assessed in primary progressive aphasia (PPA). METHODS: = 118) using the Health Utilities Index-2/3 (HUI2/3). Multiple linear regression assessed associations between HRQoL and language severity. RESULTS: Multi-attribute HUI2/3 summary scores indicated moderate to severe impairment. Scores did not differ by sex and were more severe for semantic than non-semantic PPA. Language severity scores showed significant associations with HUI multi-attribute scores and select single-attribute measures (hearing, sensation, cognition, and speech) with less language impairment associated with better functional capacity related to HRQoL. DISCUSSION: This study identified poor HRQoL in a relatively large PPA cohort. HRQoL measures aid in determining patient perspective, policy decision making, and resource allocation. Results may be used to advocate for PPA support. Highlights: Primary progressive aphasia (PPA) negatively impacts health-related quality of life.Health utilities index scores are associated with Western Aphasia Battery performance in PPA.Severity of language impairment in PPA is associated with poorer quality of life.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".