Assessment of computed tomography perfusion RAPID estimated core volume accuracy in patients following thrombectomy
Bibliographic record
Abstract
Background: Computed Tomography Perfusion (CTP) maps ischemic core volume (CV) and penumbra following a stroke; however, its accuracy in early symptom onset is not well studied. We compared the accuracy of CTP RAPID estimated CV with diffusion weighted imaging (DWI) infarct volume (IV) in patients following thrombectomy. Methods: Charts of anterior circulation large vessel occlusion post-thrombectomy cases with thrombolysis in cerebral infarction (TICI) 2b/3 reperfusion from 2017 to 2019 were reviewed. CTP time was dichotomized as 0-3 hours and ≥ 3 hours from the last known normal (LKN) cognition. The volumetric difference (VD), defined as DWI IV minus CTP CV, core volume overestimation (CVO), defined as CTP CV minus DWI IV and Alberta stroke programme early CT score (ASPECTS) were calculated. Large CV was defined as ≥ 50 ml CV. Modified Rankin Score (mRS) at 90 days were reviewed. We performed independent sample t-test and Spearman correlation coefficient test. Results: Total cases (n) were 61. In < 3 hours window from LKN (n = 27), the mean VD was 58.3 ± 0.1 ml (P = 0.990) and CVO (n = 11; 40.7%) was 39.6 ± 35.7 ml (P = 0.008). Mean large CV (n = 8) was 78.3 ± 25.4 ml with median ASPECTS of 8 [interquartile range (IQR) = 6.5-9.0] and median mRS at 90 days of 2 (IQR = 0.8-3.3). In ≥ 3 hours window from LKN (n = 34), CVO (n = 5) was uncommon and large CV had median mRS at 90 days of 5 (IQR = 4.0-6.0). Conclusion: CTP more frequently overestimates CV in patients who are < 3 hours from LKN. The treated patients with large CV in < 3 hours and > 3 hours had good and poor functional outcomes, respectively.
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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 | 0.009 |
| 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 | 0.000 |
| 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".