Regional perfusion parameters as potential indicators of parenchymal hematoma risk following reperfusion therapy for acute ischemic stroke in the extended time window
Bibliographic record
Abstract
Background: Parenchymal hematoma (PH) is a common complication of acute ischemic stroke, particularly following reperfusion therapy. Objective: This study aimed to explore the relationship between regional perfusion parameters and PH outcomes in stroke patients treated beyond the conventional time window. Design: This retrospective cohort study included patients from the CHinese Acute tissue-Based imaging selection for Lysis In Stroke-Tenecteplase (CHABLIS-T) trials and the Huashan Hospital stroke registry. Methods: Regional perfusion parameters were calculated within Alberta Stroke Program Early CT Score (ASPECTS)-defined regions of interest (ROIs). Mirror indices of cerebral blood flow (CBFmi), cerebral blood volume (CBVmi), and mean transit time were derived as the ratios of median perfusion values within ASPECTS-ROIs in the lesion and its contralateral hemisphere. Absolute time to maximum values for symptomatic ASPECTS-ROIs were also recorded. Logistic regression evaluated associations between perfusion parameters and PH outcomes, with predictive performance assessed using receiver operating characteristic (ROC) curves and area under the curve (AUC). Sensitivity analysis was conducted in patients receiving endovascular treatment (EVT) and in the trial-only population. Results: = 0.002) in the lentiform nucleus as significant predictors of PH. ROC analysis showed good discriminative performance (AUC: CBFmi 0.71 (95% CI, 0.62-0.80), CBVmi 0.70 (95% CI, 0.61-0.79)). Sensitivity analysis in patients undergoing EVT and trial-only patients drew similar results. Conclusion: Decreased CBFmi and CBVmi in the lentiform nucleus were independently associated with an elevated risk of PH, highlighting their potential utility in predicting hemorrhagic complications. Trial registration: NCT04086147, NCT04516993.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".