Comparing Predictive Value of CT Perfusion and ASPECTS for Prognosis in Large core Patients Receiving Endovascular Therapy：Focusing on CTP-based Model Development
Bibliographic record
Abstract
Abstract Background and Purpose —We aimed to compare the ability of non-contrast computed tomography Alberta Stroke Program Early CT Score (NCCT ASPECTS) with CT perfusion (CTP) combined clinical factors, in predicting functional outcome in large core patients who underwent EVT. Methods —Patients were retrospectively selected from International Stroke Perfusion Registry. We used manual NCCT ASPECTS and CTP MISTAR software to estimate ischemic status. Multivariate regression was conducted to determine independent predictors for unfavorable outcome. We then constructed a nomogram by incorporating these independent predictors. Receiver operating curve was calculated to determine its predictive value. Results —Fifty-five patients were included in the analysis. Unfavorable outcome was associated with ASPECTS and CTP in univariable analysis (P = 0.009 and P = 0.018, respectively). CTP was associated with unfavorable outcome in multivariable analysis (P = 0.02) while ASPECTS did not show significance. (P = 0.087). Multivariate logistic regression demonstrated that CTP ≥ 70mL (OR = 42.56, 95% CI 4.19 − 116.28), sex (OR = 10.55 95% CI 1.48 − 127.75), atrial fibrillation (OR = 39.60, 95% CI 3.48 − 116.23) and baseline NIHSS (OR = 9.70, 95% CI 1.76 − 80.72) were independent predictors for unfavorable outcome. CTP-combined model predicted unfavorable outcome with an AUC of 0.929 (95% CI: 0.87–0.99, P < 0.001, Se = 0.81, Sp = 0.89, PPV = 0.88, NPV = 0.83). The Hosmer − Lemeshow test showed the combined model was a good fit (P = 0.98). Conclusions —Relying solely on imaging to predict outcome is not reliable. Ischemic core volume assessed on CTP, combined with clinical indicators, is a better predicting tool for clinical outcome than ASPECTS-based model in patients with large infarct cores receiving EVT.
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".