Abstract TMP41: Predictors Of Effective Indirect Bypass In Adult Moyamoya Disease
Bibliographic record
Abstract
Background — Indirect bypass for moyamoya disease provides quite effective collaterals in most of pediatric cases, but in only a half of adult cases. However, the predictors for effective indirect bypass in adult cases are still unclear. This study was aimed to evaluate the factor that predicts the effectiveness of indirect bypass in adult moyamoya disease. Subjects and Methods — This study included totally 26 adult patients (39 sides) who underwent STA-MCA anastomosis and EDMAPS (encephalo-duro-myo-arterio-pericranial synangiosis) since 2000 in Hokkaido University Hospital. There were 6 males and 20 females. Their age ranged from 30 to 71 years. Clinical diagnosis was TIA/cerebral infarct in 30 sides, intracranial hemorrhage in 6, and asymptomatic in 3. Cerebral hemodynamics and metabolism were measured with 123 I-IMP SPECT and 15 O-gas PET before and after surgery. On postoperative cerebral angiography, the development of indirect bypass was judged as good, when it covers more than 2/3 in the MCA territory. Uni- and multivariate analysis was employed to extract the predictors for the well-developed indirect bypass. Results — The development of indirect bypass was judged as good in 21 (53.8%) of 39 sides on postoperative cerebral angiography. Univariate analysis revealed that the advanced stage (p=0.018), ischemic onset (p=0.014), low cerebral blood flow (p=0.022) were significant predictors for good indirect bypass. Multivariate analysis demonstrated that the advanced stage on preoperative cerebral angiography can predict the good development of indirect bypass (p=0.025, OR=2.314, 95% CI=1.110~4.824). Conclusions — These findings strongly suggest that indirect bypass may provide effective surgical collaterals through indirect bypass in adult patients with advanced stage of moyamoya disease.
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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 | 0.000 | 0.001 |
| 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) | 0.003 | 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".