Value of Utilizing Both Aspects and CT Angiography Collateral Score for Outcome Prediction in Acute Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND: Alberta Stroke Program Early CT Score (ASPECTS) represents the extent of irreversibly damaged tissue; while CT angiography collateral score (CTA-CS) denotes the degree of collaterals. AIMS: We investigated whether there is cumulative value in using both ASPECTS and CTA-CS for outcome prediction and attempted to determine the specific subgroup of patients who could benefit from successful reperfusion using these scores. METHODS: This is a retrospective observational study of stroke patients treated with intra-arterial reperfusion therapy for unilateral arterial occlusion in the anterior circulation. A favorable outcome was defined as modified Rankin Scale ≤ 2 at three-months. Receiver operating characteristic comparison analysis was performed to decide whether outcome predictability increases when ASPECTS and CTA-CS are used together. Classification and regression tree (CART) analysis was done to identify the variables that best predict outcome and define the specific subgroup of patients who could benefit from successful reperfusion. RESULTS: A total of 91 consecutive patients were included. Outcome predictability of ASPECTS with CTA-CS was better than that of ASPECTS (P = 0·088) or that of CTA-CS (P = 0·049). CART analysis revealed that ASPECTS > 5 was the primary determinant of favorable outcome, followed by CTA-CS > 1. Among 19 patients with ASPECTS ≤ 5, none had a favorable outcome. Successful reperfusion was associated significantly with favorable outcome in the 51 patients with ASPECTS > 5 and CTA-CS > 1, but not in the 21 patients with ASPECTS > 5 and CTA-CS ≤ 1. CONCLUSIONS: Outcome predictability improves when using ASPECTS and CTA-CS together.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".