Diagnostic and Prognostic Impact of pc‐ASPECTS Applied to Perfusion CT in the Basilar Artery International Cooperation Study
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The posterior circulation Acute Stroke Prognosis Early CT Score (pc-APECTS) applied to CT angiography source images (CTA-SI) predicts the functional outcome of patients in the Basilar Artery International Cooperation Study (BASICS). We assessed the diagnostic and prognostic impact of pc-ASPECTS applied to perfusion CT (CTP) in the BASICS registry population. METHODS: We applied pc-ASPECTS to CTA-SI and cerebral blood flow (CBF), cerebral blood volume (CBV), and mean transit time (MTT) parameter maps of BASICS patients with CTA and CTP studies performed. Hypoattenuation on CTA-SI, relative reduction in CBV or CBF, or relative increase in MTT were rated as abnormal. RESULTS: CTA and CTP were available in 27/592 BASICS patients (4.6%). The proportion of patients with any perfusion abnormality was highest for MTT (93%; 95% confidence interval [CI], 76%-99%), compared with 78% (58%-91%) for CTA-SI and CBF, and 46% (27%-67%) for CBV (P < .001). All 3 patients with a CBV pc-ASPECTS < 8 compared to 6/23 patients with a CBV pc-ASPECTS ≥ 8 had died at 1 month (RR 3.8; 95% CI, 1.9-7.6). CONCLUSION: CTP was performed in a minority of the BASICS registry population. Perfusion disturbances in the posterior circulation were most pronounced on MTT parameter maps. CBV pc-ASPECTS < 8 may indicate patients with high case fatality.
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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 | 0.007 |
| 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) | 0.001 | 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".