Post‐stroke Spasticity: Predictors of Early Development and Considerations for Therapeutic Intervention
Bibliographic record
Abstract
OBJECTIVE: The complexities of post-stroke spasticity (PSS), and the resultant difficulties in treating the disability, present a significant challenge to patients, stroke rehabilitation teams, and caregivers. Reducing the severity of spasticity and its long-term complications may be facilitated by early intervention, making identification of stroke patients at high risk for developing spasticity essential. Factors that predict which patients are at risk for the development of PSS are identified. TYPE: Systematic search and review LITERATURE SURVEY: A PubMed search of the following terms was conducted: predictors OR risk factors AND stroke AND spasticity. Studies discussing predictors of early PSS development and factors predictive of motor/functional outcomes and recovery were selected and reviewed in detail. SYNTHESIS: Several predictors of PSS have been proposed, based on studies conducted in patients within 6 months after stroke, including development of increased muscle tone, greater severity of paresis, hemihypesthesia, and low Barthel Index score. Predictors identified in later stages post-stroke (within 12 months) have also proved useful for clinicians, as has the consideration of predictors of motor and functional outcomes and recovery; yet there is a need for additional studies in this area. An understanding of these and other potential predictive factors--such as motor impairment, neurologic and sensory deficit, lesion volume and location, and associated diseases--has not progressed to the same extent and warrants further investigation. CONCLUSION: The studies discussed in this review support the notion that early identification of factors predictive of PSS should significantly affect the course of intervention, help target individuals who would benefit most from specific types and intensities of therapy, and possibly provide better motor and functional outcomes.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".