Effect of Treatment Delay on Mandibular Fracture Infection Rate
Bibliographic record
Abstract
BACKGROUND: The incidence of infection secondary to mandibular fractures ranges from 0 to 30 percent, resulting in significant sequelae. Unlike other variables that may influence infection, delayed repair is often unavoidable. The objective of this study was to accurately identify the effect of treatment delay on mandibular fracture infection rate by adjusting for confounders, thus providing strong evidence for preoperative management of these patients. METHODS: A retrospective review of mandibular fracture patients treated at the Montreal General Hospital was performed. Length of time delay between injury and operative intervention (< or = 72 hours and > 72 hours) and presence of infection were noted. Logistic regression was used to analyze the effect of treatment delay on infection, after adjustment for covariates. RESULTS: One hundred seventy-seven patients fulfilled the selection criteria and had complete records. The overall incidence of infection was 14 percent (95 percent confidence interval, 8.8 to 18.8 percent). Multiple logistic regression showed no evidence (odds ratio, 2.96; 95 percent confidence interval, 0.87 to 10.1) (p = 0.08) that treatment delay of more than 72 hours is a significant predictor of infection. The incidence of nonunion was 36 percent in the infection group (95 percent confidence interval, 17.2 to 54.8 percent) and 0 percent in the no-infection group. CONCLUSIONS: Infections following mandibular fractures frequently require extended treatment and significantly increase costs. These results show that delay of mandibular fracture treatment greater than 72 hours does not significantly increase infection risk. Repair should occur promptly after the injury. If that is not possible, the standard patient management should not be altered, as the benefits of doing so are unproven.
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 | 0.001 | 0.023 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".