Long-Term Function following Periprosthetic Fractures
Bibliographic record
Abstract
PURPOSE OF THE STUDY Clinical results of long-term follow-up after traumatic periprosthetic femur fractures and different therapies (ORIF vs. revision arthroplasty) MATERIAL AND METHODS The Visual Analog Scale (VAS), Harris-Hip-Score (HHS), Oxford-Hip-Score (OHS), Oxford-Knee-Score (OKS), Knee-Society-Score (KSS), SF-36 Questionnaire and Funktionsfragebogen Hannover (FFH) were used to evaluate outcome and functionality. Radiological examinations were performed and the Vancouver (THA) and Lewis and Rorabeck (TKA) classifications used. RESULTS 70 patients suffered a periprosthetic hip fracture (29× revision prosthesis, 41x ORIF), 23 patients underwent an ORIF due to periprosthetic fracture of a TKA (total mean age 75.2 years). 47 patients (follow-up rate 51%) were examined 40 months after surgery (mean age 72 years) (THA: 16× revision, 23× ORIF, TKA: 8× ORIF). The VAS revealed significant less pain in the group that had undergone revision hip arthroplasty than in the ORIF group: 3.9±1 vs. 5.1±1.7 (p<0.05), respectively. 5/16 patients with revision arthroplasty had excellent or good results in the HSS compared to 3/23 patients after ORIF. The OHS yielded excellent or good results in 12/16 patients after revision arthroplasty vs. 10/23 after ORIF. The VAS after ORIF in patients who suffered periprosthetic knee fractures was 4.9±2.1. 3/8 patients achieved excellent or good results according to the OKS. CONCLUSION Every functional score (HSS, OHS, FFH, SF-36) of those patients who had undergone revision arthroplasty was slightly higher and their VAS significantly lower than the scores of the patients after ORIF. Key words: periprosthetic fractures, trauma, open reduction and internal fixation, revision arthroplasty.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".