Abstract TP71: Time Burden of Perfusion Imaging
Bibliographic record
Abstract
Background: Perfusion imaging currently plays a crucial role in patient selection for endovascular thrombectomy (EVT) in the extended time window i.e. last known well (LKW) to treatment time is 6-24 hours. There is insufficient data about the treatment delays perfusion imaging may pose, especially in the real world. Methods: We retrospectively reviewed all patients who underwent EVT between August 2016 and July 2018 in a large tertiary network. The stroke triage algorithm in our network specifies CT perfusion (CTP) only for patients who present with LKW time 6-24 hours prior to presentation or when otherwise clinically indicated. Patients were classified in two cohorts based on the acquisition of CTP. We compared baseline characteristics, in addition to pre-specified time metrics of post-arrival workflow. Our aim was to compare hospital arrival to GP between CTP and non-CTP cohorts. Results: A total of 284 patients were included; 82 (28.9%) in the CTP and 202 (71.1%) in the non-CTP cohort. Patients in the CTP cohort had longer time from LKW to hospital arrival (521.3 ±434.2 mins vs 249.7 ±233.9 mins, p = 0.0001). There was no difference between the cohorts in EMS arrival versus transfers from other hospitals, or time from arrival to CT. More patients had undergone CTA at the receiving hospital in the CTP cohort (18.9% difference, 95% CI 6.6-29.7, p = 0.003). Similarly, image acquisition time was longer in the CTP cohort (33 ±46mins vs 6 ±21 mins, p = 0.0001). In the CTP cohort, 90.2% (95% CI 81.7-95.7) had Alberta Stroke Program Early CT Score (ASPECTS) ≥6. Time from hospital arrival to groin puncture (GP) was longer in the CTP cohort (126.6 ±121.4 vs 88.3 ±111.0, p = 0.01). Conclusions: While CTP was a determining factor for patient selection in extended time window trials, real world practice is hindered by longer image acquisition and interpretation times of CTP, resulting in significant treatment delay. The majority of patients undergoing EVT after CTP evaluation, would be candidates for treatment based on CT criteria for selection in less than 6h window (i.e. ASPECTS ≥6). Future studies should evaluate using CT for patient selection in extended time window, reserving CTP only for patients who would otherwise be excluded.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".