Abstract T P72: Baseline Variables Have Little Influence on Early Change in Neurological Status (ΔNIHSS) After Acute Ischemic Stroke: Basis For a Genetic Study
Bibliographic record
Abstract
Introduction: Neurological deficits can be highly unstable within the first 24 hours after acute ischemic stroke (AIS), with some patients showing dramatic improvement while others rapidly deteriorate. We are interested in genetic influences on early neurological recovery/deterioration. Here, we characterize NIHSS changes within the first 24 hours after stoke onset (ΔNIHSS) in a large cohort to determine baseline clinical variables that influence this outcome measure. Methods: AIS patients presenting to two sites (Barnes-Jewish Hospital, St Louis and Vall D’Hebron Hospital Barcelona) between 2008-2013 were prospectively enrolled. Baseline NIHSS was collected within 6 hours and again at 24 hours after symptom onset. ΔNIHSS was calculated as the difference in these stroke scale scores. Demographics, baseline comorbidities and medications, as well as acute treatment variables were recorded for each subject. Stepwise multivariable regression (SAS) was used to determine variables that significantly influence ΔNIHSS. Results: There were 954 patients enrolled (St Louis = 433, Barcelona = 521). Table 1 demonstrates the frequencies and means (SD) of the baseline variables. ΔNIHSS follows a normal distribution (figure). All baseline variables listed in table 1 were analyzed for influence on ΔNIHSS. Only baseline NIHSS (R2 = 0.0597, p<0.0001), baseline glucose (R2 = 0.0176, p=<0.0001,) and age (R2 = 0.0106, p=0.0011) independently influenced ΔNIHSS, accounting for only 8.79% of the variance. Conclusion: Baseline variables (NIHSS, glucose and age) modestly influence early neurological recovery/deterioration. However, 91% of ΔNIHSS variability remains unexplained, suggesting that other factors such as genetics, could play an important role in early outcomes following AIS. A GWAS of ΔNIHSS is currently underway.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".