Representation of People with Aphasia in Randomized Controlled Trials of Acute Stroke Interventions
Bibliographic record
Abstract
BACKGROUND: Aphasia affects up to a third of the stroke population and is associated with poor social participation and quality of life. Yet people with aphasia may be excluded from some types of stroke research due to challenges in informing, consenting, and conducting follow-up in this population. AIMS AND/OR HYPOTHESIS: We described the representation of those with aphasia in acute stroke clinical research, the level of inclusion across international trial sites, and whether there have been improvements in the inclusion of this population in recent clinical trials. METHODS: We conducted a retrospective analysis of clinical trial data from the Virtual International Stroke Trials Archive (VISTA), defining aphasia using the Best Language (item 9) domain of the National Institutes of Health Stroke Scale. We used proportional odds modeling, adjusting for age, gender, ethnicity, stroke severity, medical history, hemisphere affected by stroke, and trial eligibility criteria, to examine the associations between year, location of enrollment, inclusion, and attrition of those with aphasia. RESULTS: Data were available for 8904 patients from 10 trials; no trials listed aphasia as an exclusion criterion. At baseline, aphasia was present in 4039 (45·4%); severe/global aphasia was present in 2688 (30·2%). We observed no geographic or longitudinal disparity in the attrition of these patients at three-months. Centers in the Philippines recruited fewer people [P = 0·05, odds ratio = 0·5, 95% confidence interval (0·2, 1·0)], while centers in Central and South America included more people with severe/global aphasia [P = 0·0004, odds ratio = 2·4, 95% confidence interval (1·3, 4·3)], when compared with centers in the USA and Canada. CONCLUSIONS: Acute stroke trials have demonstrated the feasibility of including people with aphasia in stroke research; we observed geographic variations that were not entirely explained by case mix or trial eligibility criteria. Similar levels of inclusion should be sought in nonemergency stroke trials to improve the applicability of research findings to this population.
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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 | 0.295 | 0.435 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".