Core Outcomes in Aphasia Treatment Research: An e-Delphi Consensus Study of International Aphasia Researchers
Bibliographic record
Abstract
Purpose: The purpose of this article is to identify outcome constructs that aphasia researchers consider essential to measure in all aphasia treatment research. Method: Purposively sampled researchers were invited to participate in a 3-round e-Delphi exercise. In Round 1, an open-ended question was used to elicit important outcome constructs; responses were analyzed using inductive content analysis. In Rounds 2 and 3, participants rated the importance of each outcome using a 9-point rating scale. Outcomes reaching predefined consensus criteria were further analyzed using International Classification of Functioning, Disability and Health coding. Results: Eighty researchers commenced Round 1, with 72 completing the entire survey. High response rates (≥ 85%) were achieved in subsequent rounds. Consensus was reached on 6 outcomes: (a) language functioning in modalities relevant to study aims, (b) impact of treatment from the perspective of the person with aphasia (PWA), (c) communication-related quality of life, (d) satisfaction with intervention from the perspective of the PWA, (e) satisfaction with ability to communicate from the perspective of the PWA, and (f) satisfaction with participation in activities from the perspective of the PWA. Conclusions: Consensus was reached that it is essential to measure language function and specific patient-reported outcomes in all aphasia treatment research. These results will contribute to the development of a core outcome set.
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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 | 0.330 | 0.234 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.004 | 0.005 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".