The contribution of cortical strut allograft to periprosthetic femur fractures
Bibliographic record
Abstract
Objective To study the treatment of intraoperative periprosthetic femur fractures in revision THR.Methods Retrospective study was made on 32 patients who did not have infection and had periprosthetic fractures of the femur during revision THR from October 2002 to February 2007.Fractures were classified using the Vancouver classification system.There were 11 cases of type A,16 cases of type B,2 cases of type C,3 cases had both type A and type B fractures.There were 24 cases using extensively porous-coated stem supplemented by cortical strut,6 cases using extensively porous-coated stem and wires,1 case using cement stem,and 1 case fixed by cortical strut.Results Twenty-eight cases were followed up with the mean period of 23.5 months (range,3～56 months).All patients had unions of the fractures between 12 to 22 weeks after surgery (average 17.5 weeks).In one patient,the struts were fractured at 17th week.One patient experienced pain in the affected limb and two stiffness of the ipsilateral knee.The postoperation mean Harris score was 92.Conclusion The treatment of intraoperative periprosthetic fracture around the femoral implant can successfully restored function for most patients.An uncemented,extensively porous-coated stem may be a good choice.Cortical strut allograft is a useful technique for the management of periprosthetic fractures with poor host hone stock.
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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 | 0.005 |
| 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) | 0.001 | 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".