The effectiveness ORIF for neglected periprosthetic femoral fractures after hemiarthroplasty: A case report
Bibliographic record
Abstract
INTRODUCTION AND IMPORTANCE: Periprosthetic fractures are a growing concern due to the increasing frequency of primary joint replacement surgery, with total hip arthroplasty being the most common. The incidence of periprosthetic fractures after revision surgery ranges from 4 to 11 %, with up to 30 % reported after knee revision surgery. This case report aims to describe the treatment of an 81-year-old woman suffering from neglected periprosthetic femoral fracture post hemiarthroplasty. CASE PRESENTATION: An 81-year-old woman with a history of hemiarthroplasty surgery and hypertension was admitted to the ER with pain in her right thigh. She had a middle shaft femoral fracture and was scheduled for open reduction and internal fixation. Despite being fully conscious and having an average pulse rate and blood pressure, she had cardiomegaly and congestive pulmonum. Unfortunately, this patient did not receive appropriate medical treatment after it occurred for 1 month. After surgery, we evaluated the implant, and the implant stabilized the fracture. After 1-3 months after surgery, the LEFS (The Lower Extremity Functional Scale) score was found that the score increase significantly after surgery. CLINICAL DISCUSSION: The Vancouver classification system manages periprosthetic fractures by assessing location, stability, and bone quality. Type A fractures involve the trochanter, while type B fractures are diaphyseal and can extend distally. ORIF is used for subtype B1 fractures, but newer techniques offer shorter operating times and fewer complications. CONCLUSION: From this study, we can conclude that even though neglected cases procedure with ORIF promises a good outcome based on clinical evaluation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.004 |
| 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".