Plate-Assisted Intramedullary Nailing of Proximal Third Tibia Fractures
Bibliographic record
Abstract
OBJECTIVES: To report on the safety of unicortical plate stabilization in conjunction with intramedullary nailing (IMN) of proximal third tibia fractures. DESIGN: Retrospective cohort. SETTING: A Level I trauma center. PATIENTS/PARTICIPANTS: All Orthopaedic Trauma Association 41A and 42A/B/C proximal tibia fractures treated with IMN from January 2011 to May 2018 were reviewed. Fifty-three proximal tibia fractures in 50 patients were included in the study. Twenty-four patients were treated with plate-assisted reduction and IMN, and 29 were treated with IMN alone. The plate-assisted IMN cohort was subdivided into patients with plate retention and those that had the plate removed. INTERVENTION: Plate-assisted IMN and IMN only. MAIN OUTCOME MEASUREMENTS: Patients were followed up for evidence of nonunion, reduction quality, postoperative infection, and rate of implant removal. RESULTS: There were no statistically significant differences between plate-assisted IMN and IMN only for age, fracture type, mechanism of injury, quality of reduction, or implant removal rate. Open fractures were treated more often with plate-assisted IMN (88%) compared with the number of open fractures treated with IMN only (12%). There were no differences in nonunion rate or rate of postoperative infection between the 2 groups. CONCLUSIONS: Plate-assisted IMN of proximal third tibia fractures can safely be performed even in open tibia fractures with similar rates of nonunion, infection, and implant removal rates to patients treated with IMN only. LEVEL OF EVIDENCE: Therapeutic Level IV. See Instructions for Authors for a complete description of levels of evidence.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".