Abstract TP165: Association of Admission versus 24-Hour NIHSS Score With Early Ambulation After Stroke
Bibliographic record
Abstract
Introduction: Predicting who will be ambulatory within the first several days after stroke may help support resource allocation, and discharge and rehabilitation planning. The National Institutes of Health Stroke Scale (NIHSS) is commonly used to rate stroke severity. Due to medical intervention or stroke progression, the NIHSS score at admission versus 24 hours can be drastically different. We explored whether presence of leg weakness at admission or 24 hours was most predictive of early ambulation, and other NIHSS score components associated with early ambulation. Methods: A consecutive sample of patients admitted to a stroke unit at a comprehensive stroke center was included in this retrospective study. Initial and 24-hour NIHSS scores were examined in conjunction with various demographic and stroke details. Multivariable logistic regression models identified predictors of ambulation within the first week of admission. Results: Of 513 stroke patients, 273 (53%) were able to walk with or without assistance within the first 3-5 days post-stroke. The multivariable model utilizing the 24-hour NIHSS combined right and left lower extremity weakness scores had greater predictive value (area under the curve [AUC] = 0.85; 95% confidence interval [CI] 0.81 - 0.88) than the initial score (AUC = 0.74; 95% CI 0.69 - 0.78). In the multivariable analysis, 24-hour combined leg weakness (odds ratio [OR] 0.29; 95% CI 0.22 - 0.39), ataxia (OR 0.63; 95% CI 0.43 - 0.91), and sensation (OR 0.58; 95% CI 0.37 - 0.91) scores were all associated with early ambulation capacity. Conclusions: The prediction of early ambulation is more accurate after 24 hours post-stroke. Leg weakness, ataxia, and sensory loss at 24 hours are all negatively associated with ability to ambulate after stroke.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".