Intraoperative Surgical Navigation Reduces the Surgical Time Required to Treat Acute Major Facial Fractures
Bibliographic record
Abstract
BACKGROUND: Assessing bone reduction and implant placement in facial fractures is time-consuming because of limited visibility. An intraoperative navigation system allows real-time confirmation of bone positioning and implant placement on the patient's computed tomographic scan. This circumvents the visibility problem and therefore appears to shorten the surgery time. The goal of this study was therefore to determine whether intraoperative navigation reduces the surgical time required to treat patients with acute major facial fractures. METHODS: In this retrospective quasi-experimental study, 50 patients with major facial fractures were identified and randomly assigned to treatment groups. Twenty-two were treated without the use of a navigation system, and 28 were treated using navigation. The Facial frActure Severity Score (FASS) was devised to better assess and control for complexity of cases and control for possible selection bias. RESULTS: The FASS was directly linked to surgery time, whether or not navigation was used. An analysis of covariance demonstrated that the surgical time required to treat major facial fractures, taking into account the FASS, was reduced by 36.1 percent (124.8 minutes) when navigation was used. CONCLUSIONS: This study compared the surgical time required to treat patients with major facial fractures, with and without a navigation system. The use of a navigation system reduced the surgical time by 36.1 percent. This is a significant improvement in reducing the length of craniomaxillofacial procedures. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, III.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".