Effectiveness of pharmacologic interventions for prevention of cerebral hyperperfusion syndrome following bypass surgery
Bibliographic record
Abstract
BACKGROUND: Cerebral hyperperfusion syndrome (CHS) following bypass surgery is a major cause of neurological morbidity and mortality. However, data regarding its prevention have not been assorted until date. OBJECTIVE: The objective of this study was to review the literature and evaluate whether any conclusion can be drawn regarding the effectiveness of any measure on preventing bypass-related CHS. METHODS: We systematically reviewed PubMed and Cochrane Library from September 2008 to September 2018 to collect data regarding the effectiveness of pharmacologic interventions on the refers to pretreatment (PRE) of bypass-related CHS. We categorized interventions regarding their class of drugs and their combinations and calculated overall pooled estimates of proportions of CHS development through random-effects meta-analysis of proportions. RESULTS: Our search yielded 649 studies, of which 23 fulfilled inclusion criteria. Meta-analysis included 23 studies/2,041 cases. In Group A (blood pressure [BP] control), 202 out of 1,174 pretreated cases developed CHS (23.3% pooled estimate; 95% confidence interval [CI]: 9.9-39.4), Group B (BP control + free radical scavenger [FRS]) 10/263 (0.3%; 95% CI: 0.0-14.1), Group C (BP control + antiplatelet) 22/204 (10.3%; 95% CI: 5.1-16.7), and Group D (BP control + postoperative sedation) 29/400 (6.8%; 95% CI: 4.4-9.6)]. CONCLUSIONS: BP control alone has not been proven effective in preventing CHS. However, BP control along with either a FRS or an antiplatelet agent or postoperative sedation seems to reduce the incidence of CHS.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".