Abstract TP66: Optimizing CT Perfusion Thresholds for Identification of Ischemic Core in Hyperacute Stroke
Bibliographic record
Abstract
Objective: CT perfusion (CTP) estimates of infarct core are typically based on identifying regions of very low CBF (typically <30% of normal). These thresholds were optimized in patient populations that typically did not include patients scanned in the hyper-acute phase (< 60 minutes from stroke onset). Our aim was to determine if these thresholds apply to the hyper-acute phase. Methods: We conducted a retrospective review (2012 to 2017) of patients who met the following inclusion criteria: acute ischemic stroke due to an ICA or M1 occlusion, baseline CTP core (rCBF <30%) >20ml, and underwent endovascular therapy with TICI2b/3 reperfusion. Baseline ischemic core volumes were compared to final infarct volumes, measured on follow-up CT or MRI obtained >24 hours after stroke onset. Results: In 7 of 21 included patients, the ischemic core assessed on baseline CTP overestimated the final infarct by at least 10 ml. Five of these patients had witnessed onset and two were unwitnessed. The mean time from stroke onset to CTP was 0.96 hours for the seven patients with core overestimation and 3.4 hours in the 14 patients without core overcall. 6 of the 7 patients with core overcall had good outcomes (mRS of 0 to 2 at 90 days) compared to 4/14 without core overcall (p-value 0.01). For patients with core overcall, 2 additional CTP thresholds (rCBF <20% and <15%) were assessed. rCBF<20% produced core volume estimates that most closely matched the final infarct volume and did not overcall the final infarct volume. The median difference between baseline core and final infarct volume with the <20% threshold was 27 ml compared to 35 ml with <30%. Conclusion: A rCBF threshold of <30% tends to overestimate the ischemic core in patients imaged within 60 minutes of symptom onset. A threshold of <20% may be more appropriate for patients imaged in the hyper-acute phase of stroke.
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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 | 0.004 | 0.012 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".