Measuring outcomes in aphasia research: A review of current practice and an agenda for standardisation
Bibliographic record
Abstract
Background: Aphasia treatment research lacks a uniform approach to outcome measurement. A wide range of outcome instruments are used across trials and there is a lack of research evidence exploring the outcomes most important to stakeholders. This lack of standardisation produces research outcomes that are difficult to compare and combine, limiting the potential to strengthen treatment evidence through meta-analysis and data pooling. The current heterogeneity in aphasia treatment research outcome measurement may be addressed through the development of a core outcome set (COS)—an agreed standardised set of outcomes for use in treatment trials.Aims: This article aims to provide a rationale and agenda for the development of a COS for aphasia treatment research.Main Contribution: A review of the literature reveals heterogeneity in the way outcome measurement is performed in aphasia treatment research. COSs have been developed in a wide range of health fields to introduce standardisation to research outcome measurement. Potential benefits of COSs include easier comparison and combination of research outcomes, improved quality of systematic reviews and greater transparency in research reporting. The use of broad stakeholder consultation also supports the development of research outcomes that are meaningful. It is proposed that a COS for aphasia treatment research could be developed in three stages. First, consensus-based techniques would be used to reach international agreement on the outcomes that are most important to stakeholders. Second, a systematic review and meta-analysis of outcome instruments would provide synthesised evidence to support the choice of tools to most effectively capture the effects of aphasia treatments. Third, final agreement on a COS would be sought through an international consensus conference.Conclusions: There is an identified need for standardisation in the way outcomes are selected and measured in aphasia treatment research. COS development may provide an effective, consensus-based solution to this need.
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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 | 0.321 | 0.437 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.012 |
| Bibliometrics | 0.022 | 0.027 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.015 | 0.023 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 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".