Prediction of the long-term efficacy of STA-MCA bypass by DSC-PI
Bibliographic record
Abstract
Superficial temporal artery-middle cerebral artery (STA-MCA) bypass [1,2] is an important and effective type of surgical revascularization that is widely used in the treatment of ischemic cerebral artery disease. However, a means of predicting its postoperative efficacy has not been established [3,4]. The present study analyzes the correlation between preoperative perfusion parameters (obtained using dynamic susceptibility contrast-enhanced perfusion imaging, DSC-PI) and postoperative long-term prognosis (using modified Rankin Scale, mRS scores). The preoperative perfusion parameters were defined by a combination of perfusion-weighted imaging and the Alberta Stroke Program Early Computerized Tomography Score (PWI-ASPECTS) and included cerebral blood flow (CBF)-ASPECTS, cerebral blood volume (CBV)-ASPECTS, mean transit time (MTT)-ASPECTS, and time to peak (TTP)-ASPECTS. Preoperative and postoperative scores were determined for 33 patients that received a unilateral STA-MCA bypass in order to discover the most reliable imaging predictive index as well as to define the threshold value for a favorable clinical outcome. The results showed that all of the PWI-ASPECTS scores were significantly negatively correlated with clinical prognosis. Receiver operating curve (ROC) analysis of the preoperative parameters in relation to long term prognosis showed the area under curve (AUC) was maximal for the CBF-ASPECTS score (P = 0.002). A preoperative score of less than six indicated a poor postoperative prognosis (sensitivity = 74.1%, specificity = 100%, AUC = 0.843). In conclusion, preoperative PWI-ASPECTS scores have been found useful as predictive indexes for the long-term prognosis of STA-MCA bypass patients, with higher scores indicating better postoperative long-term outcomes. As the most valuable prognostic indicator, the preoperative CBF-ASPECTS score has potential for use as a major index in screening and outcome prediction of patients under consideration for STA-MCA bypass surgery.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".