Location-specific ASPECTS does not improve Outcome Prediction in Large Vessel Occlusion compared to Cumulative ASPECTS
Bibliographic record
Abstract
PURPOSE: Individual regions of the Alberta Stroke Programme Early CT Score (ASPECTS) may contribute differently to the clinical symptoms in large vessel occlusion (LVO). Here, we investigated whether the predictive performance on clinical outcome can be increased by considering specific ASPECTS subregions. METHODS: A consecutive series of patients with LVO affecting the middle cerebral artery territory and subsequent endovascular treatment (EVT) between January 2015 and July 2020 was analyzed, including affected ASPECTS regions. A multivariate logistic regression was performed to assess the individual impact of ASPECTS regions on good clinical outcome (defined as modified Rankin scale after 90 days of 0-2). Machine-learning-driven logistic regression models were trained (training = 70%, testing = 30%) to predict good clinical outcome using i) cumulative ASPECTS and ii) location-specific ASPECTS, and their performance compared using deLong's test. Furthermore, additional analyses using binarized as well as linear clinical outcomes using regression and machine-learning techniques were applied to thoroughly assess the potential predictive properties of individual ASPECTS regions and their combinations. RESULTS: Of 1109 patients (77.3 years ± 11.6, 43.8% male), 419 achieved a good clinical outcome and a median NIHSS after 24 h of 12 (interquartile range, IQR 4-21). Individual ASPECTS regions showed different impact on good clinical outcome in the multivariate logistic regression, with strongest effects for insula (odds ratio, OR 0.56, 95% confidence interval, CI 0.42-0.75) and M5 (OR 0.53, 95% CI 0.29-0.97) regions. Accuracy (ACC) in predicting good clinical outcome of the test set did not differ between when considering i) cumulative ASPECTS and ii) location-specific ASPECTS (ACC = 0.619, 95% CI 0.58-0.64 vs. ACC = 0.629, 95% CI 0.60-0.65; p = 0.933). CONCLUSION: Cumulative ASPECTS assessment in LVO remains a stable and reliable predictor for clinical outcome and is not inferior to a weighted (location-specific) ASPECTS assessment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".