Dental implant failure rates in patients with self‐reported allergy to penicillin
Bibliographic record
Abstract
BACKGROUND: In a number of previous studies, patients with reported penicillin allergies have been documented to experience higher rates of dental implant failure than those who had not reported this allergy. The authors of this study aimed to determine whether an increased risk of implant failure is associated with patient-reported penicillin allergy and which antibiotic was administered. METHODS AND MATERIALS: A retrospective study was conducted through chart review of patients who received dental implants at the New York University College of Dentistry. Participants were eligible if they received one or more dental implants at the College and provided at least 1 year of follow-up data. RESULTS: The overall implant failure rate was 12.9%. The failure rate in patients who reported no allergy to penicillin and took amoxicillin was 8.4%, while the failure rate in the allergy-reporting group was 17.1% (adjusted OR = 2.22, 95% CI = 1.44-3.44). The failure rate in allergy-reporting patients who took Clindamycin was also higher than in those who took amoxicillin (19.9%; adjusted OR = 2.9, 95% CI = 1.77-4.47) or any antibiotic other than amoxicillin (20.9%; adjusted OR = 2.77, 95% CI = 1.77-4.32). CONCLUSIONS: Significant findings included a lower implant failure rate in patients taking amoxicillin than in patients taking other antibiotics. There was a significant increase in early implant failure in allergy reporting patients.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".