Antibiotics and Facial Fractures: Evidence-Based Recommendations Compared with Experience-Based Practice
Bibliographic record
Abstract
Efficacy of prophylactic antibiotics in craniofacial fracture management is controversial. The purpose of this study was to compare evidence-based literature recommendations regarding antibiotic prophylaxis in facial fracture management with expert-based practice. A systematic review of the literature was performed to identify published studies evaluating pre-, peri-, and postoperative efficacy of antibiotics in facial fracture management by facial third. Study level of evidence was assessed according to the American Society of Plastic Surgery criteria, and graded practice recommendations were made based on these assessments. Expert opinions were garnered during the Advanced Orbital Surgery Symposium in the form of surveys evaluating senior surgeon clinical antibiotic prescribing practices by time point and facial third. A total of 44 studies addressing antibiotic prophylaxis and facial fracture management were identified. Overall, studies were of poor quality, precluding formal quantitative analysis. Studies supported the use of perioperative antibiotics in all facial thirds, and preoperative antibiotics in comminuted mandible fractures. Postoperative antibiotics were not supported in any facial third. Survey respondents (n = 17) cumulatively reported their antibiotic prescribing practices over 286 practice years and 24,012 facial fracture cases. Percentages of prescribers administering pre-, intra-, and postoperative antibiotics, respectively, by facial third were as follows: upper face 47.1, 94.1, 70.6; midface 47.1, 100, 70.6%; and mandible 68.8, 94.1, 64.7%. Preoperative but not postoperative antibiotic use is recommended for comminuted mandible fractures. Frequent use of pre- and postoperative antibiotics in upper and midface fractures is not supported by literature recommendations, but with low-level evidence. Higher level studies may better guide clinical antibiotic prescribing practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.073 | 0.332 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| 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".