Postoperative Infections in Craniofacial Reconstructive Procedures
Bibliographic record
Abstract
The rate of, and possible risk factors for, postoperative craniofacial infection is unclear. To investigate this problem, we reviewed 349 cases of craniofacial skeletal procedures performed from 1996 to 1999 at our institution. Infection rate was determined and correlated with the use of implants, operative site, and cause of deformity. The inclusion criteria consisted of all procedures requiring autologous or prosthetic implantation in craniofacial skeletal sites, as well as all procedures involving bone or cartilage resection, osteotomies, debridement, reduction and/or fixation. Procedures that did not involve bone or cartilage surgery were excluded. The criteria for diagnosis of infection included clinical confirmation and one or more of 1) intravenous or oral antibiotic treatment outside of the prophylactic surgical regimen; 2) surgical intervention for drainage, irrigation, and or debridement; and 3) microbiological confirmation. Among the 280 surgical cases that fit the inclusion criteria and had complete records, there were 23 cases of postoperative infection (8.2%). The most common site for postoperative infection was the mandible (infection rate = 16.7%). Multiple logistic regression analysis revealed gunshot wound to be the most significant predictor of postoperative infection. Additionally, porous polyethylene implantation through a transoral route was correlated with a significant risk of postoperative infection.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".