Computer-assisted Total Knee Arthroplasty After Prior Femoral Fracture Without Hardware Removal
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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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".