Locking plates for distal fibula fractures in young and elderly patients: A retrospective study
Bibliographic record
Abstract
BACKGROUND: Ankle fractures are common injuries in the young and elderly populations. To prevent post-traumatic arthritis, an anatomic reconstruction of the ankle structure is mandatory. Open reduction and internal fixation is the treatment of choice among orthopaedics. Conventional plates allow stability of the fracture if bone quality is present. Locking plates might offer an advantage for the treatment of lateral malleolar fracture in patients with comminution, severe instability, distal fractures, or osteoporotic bone. Our hospital introduced a new locking plate for fracture of the distal fibula. AIM: To evaluate locking plates in terms of outcomes and complications in young and elderly patients. METHODS: We retrospectively reviewed a total of 67 patients treated for displaced distal fibula fractures. Demographic data, number of comorbidities, use of inter fragmentary screw, complication, time of fracture healing, partial or full weight bearing, and reoperation were recorded for all patients. Clinical outcome was assessed by the American Orthopedic Foot and Ankle Society clinical scoring system. Radiographs were obtained at 4, 8, 12, 16, 20, and 24 wk until radiographic union was obtained. RESULTS: = 0.000). CONCLUSION: Locking plates provide a stable and rigid fixation in multifragmentary and comminuted fractures or in the presence of poor bone quality.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".