Plate Removal in Traumatic Facial Fractures
Bibliographic record
Abstract
In Brief Various complications can result from titanium plate internal fixation, including infection, exposure, pain, cold intolerance, and palpability. The incidence of such complications has become a topic of recent interest with the advent of resorbable plating. We undertook a retrospective review to determine complication rates of titanium fixation in a facial fracture population. Out of 266 patients with operative management of traumatic facial fracture between 1991 and 2004, 135 patients had titanium plate fixation. We evaluated 16 panfacial fractures, 22 zygomatic-orbital complex fractures, 49 midface fractures, and 48 fractures of the mandible. Overall, 33.3% (45/135) of patients had plates removed; 64.4% (29/45) of plate removals were for complications, ie, discomfort, exposure, and infection; 35.6% (16/45) were removed during secondary reconstruction. The most common complication was discomfort related to palpability, cold intolerance, and pain. This constituted 72.4% (21/29) of all plate removals for complications. Higher rates of plate discomfort were noted near the supraorbital, infraorbital, and mental foramina. Forty-five of 135 titanium plates placed for facial fractures were subsequently removed, 29 of them for complications. The most common complication was related to palpability, cold intolerance, and pain, primarily near the supraorbital, infraorbital, and mental foramina.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".