Arterial spin labeling for moyamoya angiopathy: A preoperative and postoperative evaluation method
Bibliographic record
Abstract
Abstract Objectives Studies have shown that arterial spin labeling (ASL) effectively replaces traditional MRI perfusion imaging for detecting cerebral blood flow (CBF) in patients with Moyamoya angiopathy (MMA). However, there are few reports on the relationship between neovascularization and cerebral perfusion in patients with MMA. The aim of this study is to investigate the effects of neovascularization on cerebral perfusion with MMA after bypass surgery. Methods We selected patients with MMA in the Department of Neurosurgery between September 2019 and August 2021 and enrolled them based on the inclusion and exclusion criteria. ASL imaging was used to monitor the baseline CBF level before surgery and determine the changes in cerebral vessels at postoperative 1 week and 6 months, respectively. The Alberta stroke grade, modified Rankin Scale (mRS), and digital subtraction angiography images were used to evaluate the effect of postoperative CBF status and prognosis. Ninety hemispheres from 51 patients were included in this study. There were no significant differences in the baseline data of the enrolled patients. At 1 week and 6 months post-surgery, the CBF state in the operation area was significantly changed compared with that at baseline ( P < 0.05). The preoperative Alberta score ( t = 2.714, P = 0.013) and preoperative mRS score ( t = 6.678, P < 0.001) correlated with postoperative neovascularization. Conclusion ASL is an effective method for detecting CBF and plays an important role in the long-term follow-up of patients with MMA. Combined cerebral revascularization significantly improves CBF in the operation area both in the short and long terms. Patients with lower preoperative Alberta scores and higher mRS scores were more likely to benefit from combined cerebral revascularization surgery. However, regardless of the type of patient, CBF reconstruction can effectively improve prognosis.
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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".