Poor Outcomes After Anterior Impaction Pilon Fractures
Bibliographic record
Abstract
INTRODUCTION: Pilon fractures occur through high-energy axial-loading trauma and are frequently associated with complications. The goal of this study was to assess whether anterior impaction (AI) tibial pilon fractures are associated with increased rates of posttraumatic osteoarthritis (PTOA), secondary surgeries, and lower patient-reported outcomes compared with patients with non-AI pilon fractures. METHODS: In this retrospective cohort study, 52 pilon fractures in 50 patients were included. The average follow-up was 25 months (range, 12 to 62 in non-AI and 12 to 66 in AI). The Kellgren and Lawrence (KL) score for PTOA, tibiotalar ratio for anterior-posterior talar subluxation, coronal tibiotalar angle, Patient-Reported Outcomes Measurement Information System score, and rates of secondary surgeries and infection were assessed. RESULTS: The AI group showed radiographic evidence of more advanced PTOA at the final follow-up (KL score 3.1 vs. 2.5, P = 0.021) and a higher rate of implant removal for pain (39% vs. 13%, P = 0.030). AI also had greater anterior talar subluxation on preoperative (P < 0.001) and final follow-up radiographs (P = 0.026). A higher KL score was associated with greater anterior talar displacement on preoperative (r = -0.421, P = 0.003) and final follow-up radiographs (r = -0.359, P < 0.009). No differences were seen in 1-year Patient-Reported Outcomes Measurement Information System scores. DISCUSSION: AI pilon fractures are associated with recurrent anterior talar subluxation, more severe PTOA, and a higher rate of implant removal for pain compared with non-AI fractures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".