Reevaluating antibiotic prophylaxis: insights from a network meta-analysis on dry socket and surgical site infections
Bibliographic record
Abstract
DATA SOURCES: Three databases (MEDLINE, Cochrane Library, and Scopus) were searched in December 2021 for 16 Randomised Clinical Trials (RCTs). STUDY SELECTION: Three reviewers reviewed the articles on oral antibiotic prophylaxis (ABP) for the prevention of surgical site infection (SSI) and dry socket (DS) after lower third molar (L3M) extraction using the PICO framework. From 1999 to 2021, RCTs involving healthy patients undergoing L3M extraction with ABP, placebo, or no therapy were included. Adverse effects (AEs) associated with antibiotic usage, along with the main outcomes (DS and SSI), were also documented. DATA EXTRACTION AND SYNTHESIS: Three independent investigators selected articles based on pre-established inclusion criteria, with any disagreements resolved by consensus or additional researchers. PRISMA guidelines were followed, involving initial title and abstract screening, followed by full-text evaluation. Exclusion reasons were documented, and the most recent report was included when multiple reports on the same patients were found, with no language restrictions applied. Two investigators evaluated studies quality and quality of evidence respectively using the Cochrane Collaboration tool and GRADEpro GDT. They independently extracted data, focusing on the type of extraction and the number of extracted L3M. They also detailed the use of antibiotics, including dosage, dosage regimen, timing, and duration. Among 16 articles, 15 used a parallel arm design, while one used a crossover design. The antibiotics studied included Amoxicillin+Clavulanic acid (7 articles), Amoxicillin (6), Metronidazole (2), Azithromycin (1), and Clindamycin (2), all compared with no treatment or placebo. A pairwise meta-analysis was used to combine studies with equivalent treatment (direct estimation), and a network meta-analysis compared outcome variables across different treatments (indirect comparison). RESULTS: Two included articles had a low risk of bias and the level of evidence was low according to GRADE. Pooled results supported the use of antibiotics to reduce DS and SSI following L3M extraction with a number needed to treat 25 and 18, respectively. CONCLUSIONS: Despite the fact that ABP reduces the risk of DS and SSI, it is recommended to consider systemic conditions and individual patient risk factors before prescribing antibiotics, due to global health threat.
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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 | 0.091 | 0.216 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.031 |
| Bibliometrics | 0.017 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 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".