Computer-assisted Total Knee Arthroplasty After Prior Femoral Fracture Without Hardware Removal
Why this work is in the frame
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Bibliographic record
Abstract
This study presents a consecutive series of patients who underwent total knee arthroplasty (TKA) after prior distal femoral fracture without hardware removal. The purpose of this study was to determine the effectiveness of computer-assisted TKA in patients with posttraumatic arthritis, specifically those with retained hardware after prior distal femoral fracture. The study group included a consecutive series of 16 patients who had developed posttraumatic knee arthritis after a distal femoral fracture with retention of hardware (group A). Patients in the study group were matched with patients who had undergone a computer-assisted TKA using the same implant and software (group B). The indication for TKA in all group B patients was atraumatic arthritis, and surgery was performed during the same period as that in the study group. Patients were matched for age, sex, preoperative range of motion, preoperative severity of arthritis, type and grade of deformity, and implant features. No statistically significant differences existed between the 2 study groups in terms of operative time, duration of hospital stay, or intra- and postoperative complications. At last follow-up, no statistically significant differences existed in Knee Society Scores and Western Ontario and McMaster Universities Arthritis Index scores. Implant alignment and radiological parameters were similar in both groups. This study demonstrated that posttraumatic knee arthritis after prior distal femoral fracture can be safely managed using a computer-assisted TKA without hardware removal. Comparison between the study group and a matched group with atraumatic arthritis showed similar postoperative results and complication rates.
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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 | 0.000 |
| 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) | 0.001 | 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 it