Postoperative Infections in Craniofacial Reconstructive Procedures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it