Intraoperative Anesthesiology Management and Patient Outcomes for Surgical Revascularization for Moyamoya Disease: A Review and Clinical Experience
Bibliographic record
Abstract
BACKGROUND: Moyamoya disease (MMD) is a rare cerebrovascular condition, often presenting as a headache or stroke in adults. Anesthetic management of this illness may challenge providers because it can affect the long-term neurologic outcome and hospital length of stay (LOS) in patients with MMD. MATERIALS AND METHODS: A literature search was conducted to assess etiology and epidemiology, as well as existing reports of intraoperative management of MMD. Due to sparse findings, the search was expanded to include studies of the use of intraoperative anesthetic agents during other neurosurgical procedures. We also retrospectively reviewed all MMD cases from January 1, 2009, to December 31, 2015, at Memorial Hermann Hospital-Texas Medical Center, where intraoperative management involved craniotomy and surgical revascularization. Data were collected primarily on the use of several anesthetic agents. The LOS and any adverse events were also recorded for each case. The data were divided into two equivalent case cohorts: (1) January 1, 2009, to February 18, 2013, and (2) February 19, 2013, to December 31, 2015. RESULTS: Remifentanil use notably increased between the first and second time periods while fentanyl use decreased. Desflurane usage also demonstrated an observed increase when our two cohorts were compared. Additionally, there was a decrease in the mean LOS between the first and second periods of 3.9 and 3.3 days, respectively. CONCLUSION: Increasing use of remifentanil in MMD cases could be attributed to its ability to provide more stable hemodynamics during induction, maintenance, and emergence of anesthesia when compared with fentanyl. Lower systolic pressures, diastolic pressures, and heart rates were reported in patients receiving remifentanil over fentanyl.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".