Exploring Reperfusion Following Endovascular Thrombectomy
Bibliographic record
Abstract
Background and Purpose- Cerebral perfusion in acute ischemic stroke patients is often assessed before endovascular thrombectomy (EVT), but rarely after. Perfusion data obtained following EVT may provide additional prognostic information. We developed a tool to quantitatively derive perfusion measurements from digital subtraction angiography (DSA) data and examined perfusion in patients following EVT. Methods- Source DSA images from acute anterior circulation stroke patients undergoing EVT were retrospectively assessed. Following deconvolution, maps of mean transit time (MTT) were generated from post-EVT DSA source data. Thrombolysis in Cerebral Infarction grades and MTT in patients with and without hemorrhagic transformation (HT) at 24 hours were compared. Receiver operating characteristic modeling was used to classify the presence/absence of HT at 24 hours by MTT. Results- Perfusion maps were generated in 50 patients using DSA acquisitions that were a median (interquartile range) of 9 (8-10) seconds in duration. The median post-EVT MTT within the affected territory was 2.6 (2.2-3.3) seconds. HT was observed on follow-up computed tomography in 16 (32%) patients. Thrombolysis in Cerebral Infarction grades did not differ in patients with HT from those without (P=0.575). Post-EVT MTT maps demonstrated focal areas of hyperperfusion (n=8) or persisting hypoperfusion (n=3) corresponding to the regions where HT later developed. The relationship between MTT and HT was U-shaped; HT occurred in patients at both the lowest and highest extremes of MTT. An MTT threshold <2 or >4 seconds was 81% sensitive and 94% specific for classifying the presence of HT at follow-up. Conclusions- Perfusion measurements can be obtained using DSA perfusion with minimal changes to current stroke protocols. Perfusion imaging post-recanalization may have additional clinical utility beyond visual assessment of source angiographic images alone.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".