Vasospasm Post Pituitary Surgery: Systematic Review and 3 Case Presentations
Bibliographic record
Abstract
BACKGROUND: Vasospasm is a known complication of aneurysmal subarachnoid hemorrhage and is a major cause of neurological morbidity and mortality. It is infrequently associated with pituitary adenoma surgery. We report three cases and present a systematic review of the literature with a view towards guiding neurosurgeons in the prevention and management of this complication. RESULTS: Including our experience, vasospasm complicating pituitary adenoma surgery has been documented in 29 patients (mean age of 45). All cases occurred in the setting of a postoperative hemorrhage: 21 had a subarachnoid hemorrhage and 10 had a postoperative hematoma requiring evacuation. Initial clinical appearance of delayed cerebral ischemia attributable to vasospasm occurred from postoperative Days 2-13 (most commonly Day 5). Digital subtraction angiography and medical management were the most common diagnostic and therapeutic strategies, respectively. Glasgow Outcome Scores were ≤3 in 59% of cases. Univariate logistic regression identified later diagnosis of vasospasm and surgery for hematoma evacuation to be independently associated with better outcomes. CONCLUSION: Vasospasm should be considered in the differential diagnosis of patients demonstrating altered mental or neurological status following pituitary surgery, particularly if there has been postoperative hemorrhage of any degree. Prompt treatment should be instituted to optimize outcome.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".