Does computed tomography permeability predict hemorrhagic transformation after ischemic stroke?
Bibliographic record
Abstract
AIM: To use perfusion-derived permeability-surface area product maps to predict hemorrhagic transformation following thrombolytic treatment for acute ischemic stroke. METHODS: We retrospectively analyzed our prospectively kept acute stroke database over five consecutive months for patients with symptoms of acute ischemic stroke (AIS) who had computed tomography (CT) perfusion (CTP) done at arrival. Patients included in the analyses also had to have a follow-up CT. The permeability-surface area product maps (PS) was calculated for the side of the ischemia and/or infarction and for the contralateral unaffected side at the same level. The cerebral blood flow map was used to delineate the ischemic territory. Next, a region of interest was drawn at the centre of this territory on the PS parametric map. Finally, a mirror region of interest was created on the contralateral side at the same level. The relative permeability-surface area product maps (rPS) provided an internal control and was calculated as the ratio of the PS on the side of the AIS to the PS on the contralateral side. A student t-test was performed after log conversion of rPS between patients with and without hemorrhagic transformation. Log conversion was used to convert the data into normal distribution to use t-test. For the group of patients who experienced intracranial bleed, a student t-test was performed between those with only petechial hemorrhage and those with more severe parenchymal hematoma with subarachnoid haemorrhage. RESULTS: Of 84 patients with AIS and CTP at admission, only 42 patients had a follow-up CT. The rPS derived using the normal side as the internal control was significantly higher (P = 0.003) for the 15 cases of hemorrhagic transformation (1.71 + 1.64) compared to 27 cases that did not have any (1.07 + 1.30). Patients with values above the overall mean rPS of 1.3 had an increased likelihood of subsequent hemorrhagic transformation. The sensitivity of using this score to predict hemorrhagic transformation was 71.4, the specificity was 78.6, with a positive predictive value of 62.5 and negative predictive value of 84.6. The accuracy was 76.2. The odds ratio of an event occurring with such an rPS was 9.2. Of the 15 cases of hemorrhagic transformation, there was no difference (P = 0.35) in the rPS between the eight cases of petechial and the seven cases of more severe hemorrhagic events. CONCLUSION: Pretreatment PS can predict the occurrence of hemorrhagic transformation on follow-up of AIS patients with relatively high sensitivity, specificity, positive and negative predictive value.
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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 | 0.004 |
| 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 | 0.000 | 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".