Antibiotic prophylaxis for implant placement: a systematic review of effects on reduction of implant failure
Bibliographic record
Abstract
Introduction Despite excellent reviews in the past several years, the use of antibiotics as prophylaxis for implant placement remains controversial.Aim To assess the literature on the efficacy of prophylactic antibiotics prescribed prior to and immediately following implant surgery (PIFS).Outcomes Whether administration of antibiotics reduced implant failure and post-operative complications.Design Databases searched were PubMed and Medline via Ovid (1946 to February 2018), Cochrane Library (Wiley) and Google Scholar.Materials and methods Quality assessment, meta-analysis with a forest plot and incorporated assessment of heterogeneity. A two-tailed paired t-test was performed, analysing differences in mean failure rates between groups.Results Fourteen publications were collected; 5,334 implants were placed with pre-operative antibiotics, 82 implants with antibiotics PIFS and 3,862 placed with no antibiotics. The overall risk ratio (RR) was 0.47 (95% CI 0.39-0.58), with the implant failure rates significantly affected by pre-operative intervention (Z = 7.00, P <0.00001). The number needed to treat (NNT) was 35 (95% CI 26.3-48.2). The difference between mean failure rates was statistically significant (P = 0.0335).Conclusion Administering prophylactic antibiotics reduced the risk of implant failures. Further investigations are recommended to establish a standardised protocol for the proper use of antibiotic regimen.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".