Abstract TP320: Do Aspects Or Profiles Of The NIHSS Predict Post Stroke Spasticity?
Bibliographic record
Abstract
Introduction: Post stroke spasticity (PSS) is a debilitating complication after stroke. Despite its significant impact, little is known regarding predictors of PSS. We explored the predictive value of aspects of the NIHSS after ischemic stroke (IS). Methods: A cohort of IS subjects from2005 was prospectively enrolled in an outcomes study. For each patient, we extracted baseline retrospective NIHSS values. Presence/absence of PSS was assessed at 3 months, 1 and 2 years(yrs). Each NIHSS item was dichotomized to any abnormal symptoms vs normal. Baseline demographics & previously identified risk factors for PSS were extracted from the database. Multiple logistic regression analysis with generalized estimating equations to account for repeated measures over the follow up period was used to examine associations between PSS and NIHSS items. Potential covariates - age, race, gender, smoking status, history of depression, & total NIHSS score- remained in the final model if significant at p<0.05. Models were run separately for each NIHSS item. Results: Of 460 IS patient’s, 365 (79%) had PSS data & were included in this analysis; 274 patients (75%) did not report PSS status during follow up. Of 91 patients with PSS, 54 reported PSS at 3 months, 43 at 1 yr, & 31 at 2 yrs. The NIHSS items most predictive of PSS were abnormal motor function present in: left arm (OR= 1.68, 95%CI 1.04, 2.70); left leg (OR=1.72, 95%CI 1.07, 2.75), & sensory loss (OR=1.75, 95%CI 1.07, 2.85). Multivariable models adjusting for age & total NIHSS score, left leg (adj OR=1.67, 95% CI 1.02, 2.71) remained significant; with a trend for left arm (adj OR=1.61 95% 0.99, 2.62). Age was associated with PSS (OR=0.97 95%CI 0.96, 0.99), & patients with sensory loss were younger (mean age± (SD) yrs= 62 (13) vs 69 ± (14), p <0.01). After adjusting for age, sensory loss was no longer a predictor of PSS (adj OR: 1.54, 95%CI 0.93, 2.54). Conclusion: This analysis demonstrated that among items of the NIHSS, left leg and left arm motor dysfunction were predictive of later PSS. This may reflect anatomical differences between the hemispheres or a potential enrollment bias. Sensory loss alone was also associated with later PSS but is also highly associated with age.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".