Exploring the Time to Onset and Early Predictors of Poststroke Spasticity Combined With Surface Electromyography: Protocol for a Nested Case-Control Study
Bibliographic record
Abstract
BACKGROUND: Poststroke spasticity (PSS) is a frequent sequela in patients who have experienced stroke. This form of paralysis is more prevalent compared to other poststroke sequelae and is among the most challenging and complex symptoms to manage. Surface electromyography (sEMG) can reflect the physiological information of muscles in real time and is highly beneficial in diagnosing neuromuscular diseases in clinical medicine. OBJECTIVE: This study aimed to investigate the timing of poststroke limb spasms using a nested case-control study combined with sEMG and to identify and predict factors of PSS at an early stage. METHODS: This was a nested case-control study. Participants were assessed within 24 hours of the onset of hospitalization using a standardized case report form to evaluate general patient information and clinical data related to cerebral infarction and imaging. Upon inclusion, patients were assessed after 1, 2, 4, 8, and 12 weeks, using the Modified Ashworth Scale (MAS) for spasticity severity, root mean square values from sEMG for limb spasm severity, and the simplified Fugl-Meyer (S-FM) Assessment for limb motor function. Patients who experienced spasticity within 12 weeks were assigned to the spasticity group, whereas those who did not experience spasticity were assigned to the control group. Unmatched case grouping was implemented. Data with normal distribution were analyzed using the t test, while data with nonnormal distribution were analyzed using the rank-sum test; categorical data were analyzed using the chi-square test, rank-sum test, or Fisher exact test. Logistic regression analysis was used to investigate factors affecting treatment outcomes. Data processing, analysis, and visualization were conducted using Statistical Package for the Social Sciences software (version 26.0; IBM Corp). RESULTS: This study is funded by the Chinese Association of Ethnic Medicine and began participant recruitment and registration in November 2023. The study is currently ongoing, with 66 participants enrolled as of March 2025. CONCLUSIONS: This study selected a diagnostic method combining sEMG and subjective scales to determine PSS, aiming to eliminate diagnostic errors caused by subjective assessments. This study adopted a nested case-control study method, which has minimal information bias, allowing for the inference of causal relationships between exposure and disease. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR2300077121; https://www.chictr.org.cn/showproj.html?proj=205037. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/65829.
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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 | 0.029 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".