Challenges of Estimating Accurate Prevalence of Arm Weakness Early After Stroke
Bibliographic record
Abstract
Background. Recent studies have reported lower statistics of upper limb (UL) weakness (48-57%) compared to widely cited values collected over 2 decades ago (70-80%). Objective. To explore potential factors contributing to the accuracy of prevalence values of UL weakness using a case study from a single regional centre. Methods. All patients admitted to the acute stroke unit with suspected diagnosis of stroke were screened from February 2016 to August 2017. Upper limb weakness was captured (a) prospectively using the Shoulder Abduction and Finger Extension (SAFE) score performed by unit physical therapists within 7 days post-stroke and (b) retrospectively via chart review using the National Institutes of Health Stroke Scale (NIHSS) arm score at admission and 24 hours post-admission. Results. A total of 656 patients were admitted with a first-ever stroke, and 621 (95%) individuals were administered the SAFE score. A total of 40% of individuals had UL weakness using the SAFE score (SAFE ≤8) at a mean time of 1.9 (SD 1.5) days post-stroke. In the same sample, 57% and 49% had UL weakness using the admission and 24-hour post-admission NIHSS arm score, respectively. Conclusions. The accuracy of population-level UL weakness prevalence values can be affected by weakness measure and score cut-off, time post-stroke weakness is captured, sample characteristics and use of single or multiple sites. Researchers using prevalence values for clinical trial planning should consider these attributes when using prevalence data for estimating recruitment rates and resource needs.
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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 | 0.093 | 0.316 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".