Predictors and Outcomes of ASPECTS Decay during Interfacility Transfers in Patients with Large Vessel Occlusion
Bibliographic record
Abstract
ABSTRACT Background: Some patients with large vessel occlusion (LVO) are first evaluated at primary stroke centers (PSCs) before transfer to a comprehensive stroke center (CSC) for endovascular thrombectomy (EVT). A subgroup of these patients experiences rapid infarct growth, also known as “imaging decay,” during transfer, limiting the benefit from intervention. We evaluated the incidence, predictors and outcomes of imaging decay in this subgroup. Methods: The present study was an analysis of all patients with anterior circulation LVO at PSCs in Northern Alberta and transferred to the CSC in the University of Alberta Hospital in Edmonton for EVT. The Alberta Stroke Program Early CT Score (ASPECTS) decay was defined as ≥ 2 ASPECTS points decrement at CSC compared to PSC. The primary outcome was 90-day home time. Results: 182 patients were included. Median time between baseline and follow-up CTs was 250.5 (IQR 163–324.25) minutes. Out of the 182 patients, 66 patients (36%) had ASPECTS decay, and 32 of 66 patients (48%) underwent EVT. Poor collateral score was strongly associated with ASPECTS decay (OR = 0.35, [0.21–0.59], p < 0.001). Patients with ASPECTS decay have a significantly lower 90-day home time ( β = –0.32, [–4.6 to –36.4], P < 0.001) and higher risk of 90-day mortality (OR = 4.9, [2.4–10.0], P < 0.001) and in-hospital death (OR = 3.8, [1.2–12.3], P = 0.03). Conclusions: For patients with LVO transferred for thrombectomy, a third of our patients developed ASPECTS decay. Collateral blood flow was the main determinant of ASPECTS decay during interfacility transfers. Decay is strongly associated with poor functional outcomes.
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 | 0.000 | 0.003 |
| 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 | 0.000 |
| 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".