New Picture Stimuli for the NIH Stroke Scale: A Validation Study
Bibliographic record
Abstract
BACKGROUND: The National Institutes of Health Stroke Scale is a widely accepted tool for structured graded neurological examination of stroke or suspected stroke in the hyperacute setting. Concerns have arisen about the use of its picture stimuli in a contemporary and global health context. Here, we present new stimuli prepared to serve the needs of stroke providers worldwide: the precarious painter image description and updated objects for naming. METHODS: This was a validation study of 101 healthy fluent English speakers. Participants were reached by the Johns Hopkins Outpatient Center, the University of South Carolina, and Prisma Health from 2022 to 2023 and included residents of the United States, Germany, Canada, the United Kingdom, Australia, and Zambia. Participants were recorded in person or via video conferencing when asked to describe the new picture, while a subset named seven illustrations. Multivariate analyses of variance were used for primary analyses. In a complementary investigation, 299 attendees of the 2023 International Stroke Conference were asked about their preference for the existing or new stimuli and why. RESULTS: Each of the 44 content units from the picture description was included by at least 5% of respondents in the demographically representative subsample. Performance was similar across healthy participants irrespective of age, sex, race, ethnicity, or education. Typical descriptions were characterized by an average of 23 content units (SD=5) conveyed with 167 syllables (SD=79). The new naming stimuli were recognized by 100% of participants from many countries as being familiar and identifiable, and names provided in response to the task were highly convergent. The majority of stroke health care providers preferred both the precarious painter and naming stimuli. CONCLUSIONS: The description of the new National Institutes of Health Stroke Scale picture, the precarious painter, results in rich samples among healthy speakers that will provide an appropriate basis for the detection of language deficits.
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".