Prevention of Postdental Procedure Bacteremia: A Network Meta-analysis
Bibliographic record
Abstract
Postdental procedure bacteremia is common and troublesome. The comparative efficacy of multiple prophylactic interventions is unclear. We compared the efficacy of interventions for the prevention of postdental procedure bacteremia. We conducted a review of ClinicalKey, Cochrane CENTRAL, Embase, ProQuest, PubMed, ScienceDirect, Web of Science, and ClinicalTrials.gov from inception to December 4, 2018. Randomized controlled trials that evaluated prophylactic interventions for the prevention of postdental procedure bacteremia were eligible. The primary outcome was the incidence of postdental procedure bacteremia. A total of 24 trials were included with 2,147 participants. Our network meta-analysis demonstrated that intravenous administration of 1,000/200 mg of amoxicillin/clavulanate provided the least incidence of postdental procedure bacteremia among all the prophylactic interventions (odds ratio = 0.03, 95% CI = 0.00 to 0.63) as compared with the placebo/controls. Oral 3 g of amoxicillin had the least incidence of postdental procedure bacteremia among all oral or topical forms of prophylactic interventions (odds ratio = 0.10, 95% CI = 0.02 to 0.44) as compared with the placebo/controls. No serious adverse events, such as anaphylactic shock, mortality, and the development of antibiotic-resistant bacteria, were reported. None of the included subjects were of high risk of infectious endocarditis. Our network meta-analysis demonstrates that intravenous amoxicillin/clavulanate and oral amoxicillin might be the best prophylactic interventions in preventing postdental procedure bacteremia among all the oral/topical forms of interventions for the overall populations.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.037 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".