Abstract W MP10: The Prognostic Value of Age and NIH Stroke Scale in Predicting the Outcomes of Endovascular Stroke Therapy in the STAR Registry; Validation of the SPAN-100 score
Bibliographic record
Abstract
Background: Age and stroke severity are inversely correlated with the odds of favorable outcome after ischemic stroke. The Stroke Prognostication using Age and NIH Stroke Scale (SPAN) index identified that patients with a SPAN index 100 or more (SPAN-100 positive) did not benefit from IV tPA in the NINDS trial. The effect of successful reperfusion on the prognostic value of this score is not known. Methods: The SPAN index was calculated for patients in the prospective Solitaire FR Thrombectomy for Acute Revascularisation "STAR" study: an international single-arm multi center cohort for anterior circulation stroke. The proportion with favorable outcome (90-day mRS score ≤ 2) was compared between SPAN-100 positive vs. negative patients. Results: Of the 202 patients enrolled, 196 had non-missing baseline NIHSS. Fifteen (7.7%) patients were SPAN -100 positive. There was no difference in the rate of successful reperfusion (TICI2b or 3) between SPAN-100 positive vs. negative groups (93.3% vs. 82.8% respectively, p=0.3). SPAN-100 positive patients had a significantly lower proportion of favorable clinical outcome (26.7% vs. 60.8% in SPAN-100 negative, p=0.01) (Figure). In a univariable analysis, SPAN-100 positive status was associated with lower odds of favorable outcome (OR 0.23, CI95 0.07 to 0.77; p=0.02). In a multivariable logistic regression, only baseline ASPECTS and time from onset to revascularization were significant predictors of favorable outcome. Conclusion: A significantly lower proportion of patients with a positive SPAN-100 index achieved favorable outcome at 90 days. In our cohort, this effect was accounted for by delays in the time from onset to revascularization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".