An international core outcome set for primary progressive aphasia (COS‐PPA): Consensus‐based recommendations for communication interventions across research and clinical settings
Bibliographic record
Abstract
INTRODUCTION: Interventions to treat speech-language difficulties in primary progressive aphasia (PPA) often use word accuracy as a highly comparable outcome. However, there are more constructs of importance to people with PPA that have received less attention. METHODS: Following Core Outcome Set Standards for Development Recommendations (COSSTAD), this study comprised: Stage 1 - systematic review to identify measures; Stage 2 - consensus groups to identify important outcome constructs for people with PPA (n = 82) and care partners (n = 91); Stage 3 - e-Delphi consensus with 57 researchers. RESULTS: The systematic review identified 84 Outcome Measurement Instruments. Core outcome constructs identified included: (1) Participate in conversations with family and friends, (2) get words out, (3) be more fluent, (4) convey a message by any means, and (5) understand what others are saying. Researchers were unable to reach a consensus on measurement instruments. DISCUSSION: Further work is required to develop appropriate measurement instruments that address all core outcome constructs important to key stakeholders. HIGHLIGHTS: We introduce new symptom-led perspectives on primary progressive aphasia (PPA). The focus is on non-fluent/agrammatic (nfvPPA) and semantic (svPPA) variants. Foregrounding of early and non-verbal features of PPA and clinical trajectories is featured. We introduce a symptom-led staging scheme for PPA. We propose a prototype for a functional impairment scale, the PPA Progression Planning Aid.
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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 | 0.461 | 0.511 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.024 |
| Bibliometrics | 0.026 | 0.016 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.013 | 0.021 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".