Treatment of periprosthetic femoral fractures following hip arthroplasty
Bibliographic record
Abstract
Objective To discuss an accuracy preoperative planning and clinical effects according to the periprosthetic femoral fracture following hip arthroplasty.Methods We retrospectively review 45 periporsthetic femoral fracture treated at Taichung Veteran General Hospital from Jan 1988 to Jun 2001.Results data include the medical record and X-ray film. The patient were classified by Vancouver classification. There were 7 type A, 24 type B1, 7 type B2, 4 type B3, and 3 type C cases. The option of treatments including open reduction with internal fixation or revision total hip arthroplasty with or without wire, cable, Dall-Miles plate, structural allograft or chips of allogenous bone graft. The prosthesis used in revision THR are long stem prosthesis or modular type stem. Results Type A and B1 have high union rate, though we must be careful to the length of the plate. The alignment of revision surgery is the key point of the treatment of type B2. In complicated type B3 and C, high failure rate was noted. We analysis the results and complication of the methods used in treating each type of fracture. Construct an algorithm for treating the complicated injury making it more logically and simple.Conclusions Treatment of Periprosthetic Femoral Fractures Following Hip Arthroplasty is a complicated issue. Clearly read the personality of the fracture, familiar all options of treatment, properly select a method, and construct a logically treatment flow chart, are the criteria for successful treatment.
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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 | 0.001 |
| 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".