Early post-endovascular treatment contrast extravasation on dual-energy CT is associated with clinical and radiological stroke outcomes: A 10-year single-centre experience
Bibliographic record
Abstract
OBJECTIVE: To determine the association between early post-endovascular treatment (EVT) contrast extravasation (CE) on dual-energy CT (DECT) and stroke outcomes. METHODS: EVT records in 2010-2019 were screened. Exclusion criteria included the occurrence of immediate post-procedural intracranial haemorrhage (ICH). Hyperdense areas on iodine overlay maps were scored according to the Alberta Stroke Programme Early CT Score (ASPECTS), thus forming a CE-ASPECTS. Maximum parenchymal iodine concentration and maximum iodine concentration relative to the torcula were recorded. Follow-up imaging was reviewed for ICH. The primary outcome measure was the modified Rankin Scale (mRS) at 90 days. RESULTS: Out of 651 records, 402 patients were included. CE was found in 318 patients (79%). Thirty-five patients developed ICH on follow-up imaging. Fourteen ICHs were symptomatic. Stroke progression occurred in 59 patients. Multivariable regression showed a significant association between decreasing CE-ASPECTS and the mRS at 90 days (adjusted (a)cOR: 1.10, 95% CI: 1.03-1.18), NIHSS at 24-48 h (aβ: 0.57, 95% CI: 0.29-0.84), stroke progression (aOR: 1.14, 95% CI: 1.03-1.26) and ICH (aOR: 1.21, 95% CI: 1.06-1.39), but not symptomatic ICH (aOR 1.19, 95% CI: 0.95-1.38). Iodine concentration was significantly associated with the mRS (acOR: 1.18, 95% CI: 1.06-1.32), NIHSS (aβ: 0.68, 95% CI: 0.30-1.06), ICH (aOR: 1.37, 95% CI: 1.04-1.81) and symptomatic ICH (aOR: 1.19, 95% CI: 1.02-1.38), but not stroke progression (aOR: 0.99, 95% CI: 0.86-1.15). Results of the analyses with relative iodine concentration were similar and did not improve prediction. CONCLUSIONS: CE-ASPECTS and iodine concentration are both associated with short- and long-term stroke outcomes. CE-ASPECTS is likely a better predictor for stroke progression.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".