Predictors of Spasticity 3–6 Mos After Stroke
Bibliographic record
Abstract
OBJECTIVE: The aim of the study is to identify predictors of poststroke spasticity (modified Ashworth Scale scores, ≥1) at 3-6 mos after stroke. DESIGN: A 5-yr (2015-2020) retrospective cohort of patients who attended inpatient stroke rehabilitation in Southwestern Ontario, Canada, were included. Sociodemographic, clinical, stroke-related, rehabilitation-related, and outcome measure data were extracted from paper charts and electronic databases. RESULTS: Of the 922 individuals attending inpatient stroke rehabilitation, 606 (55.8% males; mean age = 70.9 ± 14.2 yrs) returned for an outpatient visit. Most patients had a first ever ( n = 518; 85.5%), ischemic ( n = 470; 77.6%) stroke with hemiplegia ( n = 449, 74.1%). A total of 20.3% ( n = 122) of patients had developed poststroke spasticity by 4 mos after stroke. A binary logistic regression significantly predicted poststroke spasticity (χ 2 (6) = 111.696, P < 0.0001) with good model fit (χ 2 (8) = 12.181, P = 0.143). There were six significant poststroke spasticity predictors: hemorrhagic stroke ( P = 0.049), younger age ( P < 0.001), family history of stroke ( P = 0.015), Functional Independence Measure admission score ( P < 0.001), use of selective serotonin reuptake inhibitors ( P = 0.044), and hemiplegia ( P < 0.001). CONCLUSIONS: Patients should be monitored closely for poststroke spasticity after discharge from stroke rehabilitation and throughout the care continuum.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".