Contribution and Additional Impact of Imaging to the SPAN-100 Score
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Stroke Prognostication by Using Age and NIHSS score (SPAN-100 index) facilitates stroke outcomes. We assessed imaging markers associated with the SPAN-100 index and their additional impact on outcome determination. MATERIALS AND METHODS: Of 273 consecutive patients with acute ischemic stroke (<4.5 hours), 55 were characterized as SPAN-100-positive (age +NIHSS score ≥ 100). A comprehensive imaging review evaluated differences, using the presence of the hyperattenuated vessel sign, ASPECTS, clot burden score, collateral score, CBV, CBF, and MTT. The primary outcome assessed was favorable outcome (mRS ≤ 2). Secondary outcomes included recanalization, lack of neurologic improvement, and hemorrhagic transformation. Uni- and multivariate analyses assessed factors associated with favorable outcome. Area under the curve evaluated predictors of favorable clinical outcome. RESULTS: Compared with the SPAN-100-negative group, the SPAN-100-positive group (55/273; 20%) demonstrated larger CBVs (<0.001), poorer collaterals (P < .001), and increased hemorrhagic transformation rates (56.0% versus 36%, P = .02) despite earlier time to rtPA (P = .03). Favorable outcome was less common among patients with SPAN-100-positive compared with SPAN-100-negative (10.9% versus 42.2%; P < .001). Multivariate regression revealed poorer outcome for SPAN-100-positive (OR = 0.17; 95% CI, 0.06-0.38; P = .001), clot burden score (OR = 1.14; 95% CI, 1.05-1.25; P < .001), and CBV (OR = 0.58; 95% CI, 0.46-0.72; P = .001). The addition of the clot burden score and CBV improved the predictive value of SPAN-100 alone for favorable outcome from 60% to 68% and 74%, respectively. CONCLUSIONS: SPAN-100-positivity predicts a lower likelihood of favorable outcome and increased hemorrhagic transformation. CBV and clot burden score contribute to poorer outcomes among high-risk patients and improve stroke-outcome prediction.
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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 | 0.006 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".