Evaluations of relevant factors affecting preoperative and postoperative CT perfusion with ASPECTS in patients receiving stent implantation for middle cerebral artery stenosis
Bibliographic record
Abstract
Objective To investigate relevant factors affecting cerebral CT perfusion (CTP) before and after stent implantation in patients with middle cerebral artery stenosis. Methods 25 cases of ischemic cerebrovascular disease patients who received middle cerebral artery stent implantation in Beijing Tiantan Hospital were chosen, Alberta Stroke Program Early CT Score(ASPECTS) was used to evaluate the time to peak (TTP) of CTP before and after stent implantation, and factors which might potentially affect preoperative and postoperative CTP assessments were analyzed. Results Preoperative TTPs for all of the 25 patients were remarkably delayed than contral cerebral hemisphere, and postoperative TTPs for all of the 25 patients were improved compared with preoperative results. The preoperative TTP ASPECTS score was 2.32 ±1.31, postoperative ASPECTS score was 8.28 ±1.65, and the paired t test demonstrated that there was a statistically significant difference (P 0.001). Preoperative ASPECTS score was negatively correlated with middle cerebral artery stenosis with a correlation coefficient of - 5.78. Relevant factors which may affect the improvement of CTP after stent implantation were analyzed, significant difference was only found between the presence and absence of good collateral circulation (P = 0.033). Preoperative stenosis rate was positively correlated with the improvement of ASPECT score for TTP, with a correlation coefficient of 1.137 (P = 0.001). Conclusion TTP is a sensitive assessment for the evaluation of middle cerebral artery stent implantation, and preoperative and postoperative TTP may be quantitatively evaluated with ASPECT score. Patients with severe stenosis or without good collateral circulation may benefit more from stent implantation.
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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".