Abstract TP108: Clinical Utility of the Alberta Stroke Program Early CT Score: A Survey Comparing Comprehensive Stroke Centers
Bibliographic record
Abstract
Introduction: The Alberta Stroke Program Early CT Score (ASPECTS) is often used in considering whether patients are appropriate for thrombolysis and/or thrombectomy after acute ischemic stroke (AIS). We hypothesized that while clinical guidelines recommend ASPECTS evaluations in AIS, ASPECTS use is low in practice. We also explored differences in ASPECTS use between high and low volume comprehensive stroke centers (CSC). Methods: We surveyed United States CSC from 2021-2022 regarding ASPECTS utilization in evaluating eligibility for thrombolysis and/or thrombectomy in patients with suspected or confirmed large vessel occlusion. We asked whether ASPECTS was routinely used, if it was the preferred primary modality for final decision making, and whether an ASPECTS < 6 excluded a patient from thrombectomy consideration. Survey responses were divided between large (Annual volumes of ischemic strokes >600) and small CSC ( < 600). Chi square analysis was performed on this data. Results: Thirty-nine CSC completed the survey. Of these, 21 were large CSC and 18 were small CSC. Of all CSC, 31% did not use ASPECTS at all. Seventy-four percent preferred automated mismatch software over ASPECTS as the primary modality in final decision making. Thirty six percent of CSC noted that an ASPECTS < 6 would exclude a patient from consideration for thrombectomy. There was no relationship between CSC size and ASPECTS use [X 2 (1, N = 39) = 0.71, p < .05]. There was also no relationship between CSC size and whether ASPECTS was the preferred primary final decision making modality [X 2 (1, N = 39) = 0.23, p < .05]. Additionally, there was no relationship between CSC size and whether ASPECTS < 6 excluded patients from thrombectomy consideration [X 2 (1, N = 39) = 0.73, p < .05]. Conclusion: In practice, the clinical utility of ASPECTS for AIS evaluation is low. Case volume experience also has no apparent impact on ASPECTS utilization. Our preliminary survey reveals low use of ASPECTS, and a preference for mismatch software in choosing AIS candidates for intervention. This discrepancy suggests significant differences between clinical guidelines and actual practice.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".