Comparing the Prognostic Impact of Age and Baseline National Institutes of Health Stroke Scale in Acute Stroke due to Large Vessel Occlusion
Bibliographic record
Abstract
Background and Purpose: Little is known about the combined effect of age and National Institutes of Health Stroke Scale (NIHSS) in endovascular treatment (EVT) for acute ischemic stroke due to large vessel occlusion, and it is not clear how the effects of baseline age and NIHSS on outcome compare to each other. The previously described Stroke Prognostication Using Age and NIHSS (SPAN) index adds up NIHSS and age to a 1:1 combined prognostic index. We added a weighting factor to the NIHSS/age SPAN index to compare the relative prognostic impact of NIHSS and age and assessed EVT effect based on weighted age and NIHSS. Methods: We performed adjusted logistic regression with good outcome (90-day modified Rankin Scale score 0–2) as primary outcome. From this model, the coefficients for NIHSS and age were obtained. The ratio between the NIHSS and age coefficients was calculated to determine a weighted SPAN index. We obtained adjusted effect size estimates for EVT in patient subgroups defined by weighted SPAN increments of 3, to evaluate potential changes in treatment effect. Results: We included 1750/1766 patients from the HERMES collaboration (Highly Effective Reperfusion Using Multiple Endovascular Devices) with available age and NIHSS data. Median NIHSS was 17 (interquartile range, 13–21), and median age was 68 (interquartile range, 57–76). Good outcome was achieved by 682/1743 (39%) patients. The NIHSS/age effect coefficient ratio was ([−0.0032]/[−0.111])=3.4, which was rounded to 3, resulting in a weighted SPAN index defined as ([3×NIHSS]+age). Cumulative EVT effect size estimates across weighted SPAN subgroups consistently favored EVT, with a number needed to treat ranging from 5.3 to 8.7. Conclusions: The impact on chance of good outcome of a 1-point increase in NIHSS roughly corresponded to a 3-year increase in patient age. EVT was beneficial across all weighted age/NIHSS subgroups.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".