Burst Suppression for ICP Control
Bibliographic record
Abstract
BACKGROUND: The goal of our study was to perform a systematic review of the literature to determine the effect that burst suppression has on intracranial pressure (ICP) control. METHODS: All articles from MEDLINE, BIOSIS, EMBASE, Global Health, Scopus, Cochrane Library, the International Clinical Trials Registry Platform (inception to January 2015), reference lists of relevant articles, and gray literature were searched. The strength of evidence was adjudicated using both the Oxford and the Grading of Recommendation Assessment Development and Education (GRADE) methodology. RESULTS: Seven articles were considered for review. A total of 108 patients were studied, all receiving burst suppression therapy. Two studies failed to document a decrease in ICP with burst suppression therapy. There were reports of severe hypotension and increased infection rates with barbiturate-based therapy. Etomidate-based suppressive therapy was linked to severe renal dysfunction. CONCLUSIONS: There currently exists both Oxford level 2b and GRADE C evidence to support that achieving burst suppression reduces ICP, and also has no effect on ICP, in severe traumatic brain injury. The literature suggests burst suppression therapy may be useful for ICP reduction in certain cases, although these situations are currently unclear. In addition, the impact on patient functional outcome is unclear. Further prospective study is warranted.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".