Kortikale Strut Grafts, eine Alternative zur konventionellen Plattenosteosynthese bei periprothetischer Femurfraktur
Bibliographic record
Abstract
AIM: In periprosthetic fracture therapy plating has a failure rate between 30 to 50 %. Cortical allografts proved to unite constantly in revision surgery of the femoral shaft. The hypotheses to be answered in this study was whether the use of cortical allografts increases the success rate in contrast to plating. METHOD: A retrospective case control study has been done analysing the results in periprosthetic fractures of the femur around or below the tip of a stable femoral component (Vancouver B1, Mont III + IV). RESULTS: In 12 plating cases an average of 2.67 units of blood was transfused (0 - 6). In the follow-up two refractures, one loosening of the femoral component and one varus deformity greater than 10 degrees occurred. In 6 patients a total of 9 reoperations had to be done. According to the classification of Mont the results were excellent in 50 %, good in 16.7 % and poor in 33.3 %. 7 patients with strut grafts received an average of 1,43 units of blood transfused (0 - 3). In one patient with a 16 degrees varus deformity of the femur, due to multiple previous operations, a deformity of 21 degrees had to be accepted. Until now, no further operation had to be done in this group. The results were classified as excellent in 5 cases (71.4 %) and good in the other 2 (28,6 %). There was no poor result. The difference between the groups was statistically significant in the number of reoperations (p < 0. 05). CONCLUSION: In this study, with a low number so far, the strut graft group reached better results in every single variable, with a significant reduction of reoperations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.004 | 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 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".