Influence of a single preoperative dose of antibiotics on the early implant failure rate. A randomized clinical trial
Bibliographic record
Abstract
BACKGROUND: The use of a preoperative single dose of antibiotics as routine in conjunction with implant surgery is controversial, in light of the unclear effect on early implant failure rate and risk for development of resistant bacterial strains. PURPOSE: This randomized clinical trial compared the early implant failure rates in two different patient cohorts: One group receiving a single dose of preoperative antibiotics (AB group) and one group receiving no antibiotics, prior to implant surgery (noAB group). MATERIALS AND METHODS: Patients were referred for treatment at four specialist clinics in the county council of West Sweden, Vastra Gotaland and randomly assigned into one of the two groups. A total of 447 patients received 963 implants were included in the study. Of these, 223 patients (535 implants) belonged to the AB-group and 224 patients (428 implants) to the noAB-group. Four commercial implant brands were utilized, albeit one system was only represented with four implants. The outcome was evaluated after 4 months using either a one-stage or two-stage procedure. The surgical procedures were performed by experienced implant surgeons and the surgical protocol for implant placement follows standard. Failure was defined as removal of an implant for any reason. The study outcomes were statistically analyzed to evaluate the differences between the two groups. RESULTS: Twelve implants failed in 11 patients for the AB group, and 32 implants failed in 29 patients for the noAB group. Preoperative antibiotics, AB group, had significantly (P < 0.0011) lower implant failure 2.2% compared to 7.5% in the noAB group analyzed on implant level adjusted for dependence within patients, OR = 0.30, 95% confidence interval (0.14-0.62). CONCLUSION: Administration of a single dose of antibiotics in conjunction with implant placement surgery resulted in a statistically significant lower early implant failure rate compared to when no antibiotics were used.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".