Assessment of cognition in aphasia: Perspectives from clinicians and researchers
Bibliographic record
Abstract
People with aphasia may present with cognitive impairments beyond language. In this population, reliance on language-based assessments of cognition may lead to unreliable metrics of cognitive performance across clinical and research settings. We investigated the underlying philosophy and practice of assessing cognition in people with aphasia. An e-survey was developed for multidisciplinary clinicians and researchers. Snowball sampling was used to recruit international participants. The e-survey comprised 29 items (a mix of multiple-choice and open-ended items) addressing definitions of cognition, assessment of cognition, tools used to assess cognition and participant demographics. Data were analysed using descriptive statistics and thematic analysis. 291 respondents participated from a range of disciplines and countries. Over 80% of respondents considered it important to assess attention, executive functions, learning and memory. The main barrier to assessment was the lack of appropriate tools available for people with aphasia. Responses indicated that whilst professionals felt that understanding the interaction between language and cognition in aphasia was important for providing optimal care. This study highlights the need for better awareness and training in the assessment of cognition in people with aphasia, and for psychometrically robust assessments, appropriate for the assessment of cognition in the presence of aphasia.
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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 | 0.104 | 0.161 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.001 | 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".