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 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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.015 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".