Choice of Internal Rigid Fixation Materials in the Treatment of Facial Fractures
Bibliographic record
Abstract
The surgical treatment of craniomaxillofacial trauma involves the restoration of both form and function via a complex interplay between the facial bony skeleton and its soft tissue envelope. However, it was not until the introduction of open reduction and internal rigid fixation techniques for the facial skeleton that the basic orthopedic principles of accurate fracture reduction, bone fixation, and healing could be applied. The latter introduced the unprecedented ability to repair unstable and/or displaced bony fractures of the face, providing a stable foundation upon which to reestablish preinjury soft tissue contour. Advances in the science of internal fixation, improvements in available plating materials and equipment, refinements in exposures to the facial skeleton, and an increase in the volume of facial trauma all fueled the rapid expansion of use of rigid internal fixation for facial fractures in the 1980s.1 With growing experience, surgeons came to appreciate the utility of metallic internal rigid fixation systems, along with the potential pitfalls and complications.2,3,4,5 In addition, the permanence of metallic implants spawned questions of long-term safety,2,5,6,7,8 rates and need for removal,3,9,10 and risks in the growing pediatric skeleton.11,12,13,14,15 Aimed at addressing these concerns, manufacturers began research and development of resorbable rigid fixation systems, which more recently are gathering interest in the management of facial trauma. With this in mind, the authors have attempted to summarize and compare the current data describing use of either metallic or resorbable fixation systems for the treatment of facial fractures in an effort to educate surgeons faced with selecting between these two options. Factors such as complication rates, cost, efficacy, and availability are all considered and summarized in this article.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".