Osteosynthesis and outcomes of traumatic periprosthetic femoral fractures after total hip arthroplasty
Bibliographic record
Abstract
PURPOSE: The aim of this retrospective study was to investigate the treatment of traumatic periprosthetic femoral fractures with open reduction and internal fixation. The outcomes with the use of the surgical techniques were also reported. METHODS: Between September 2017 and September 2019, 25 patients with traumatic periprosthetic femoral fractures were managed by open reduction and internal fixation in Ain Shams University Hospital, Egypt. The fixation methods were selected based on the surgeon's preference. Outcomes were assessed using the Harris Hip Score, visual analogue score (VAS) for pain, and EuroQol 5 Dimensions - 5 Level (EQ5D-5L) prior to and after surgery. Patients were regularly followed up for one year. A P value < 0.05 was considered to be statistically significant. RESULTS: The mean age at surgery was 77 years (range, 51 to 95 years), 64% (n = 16) were females. According to the Vancouver classification, there were 1 type AG, 15 type B1, and 9 type C fractures. Postoperative complications included wound site infection (n = 2) and non-union (n = 1). The mean pre-trauma Harris Hip Score was 77.44 ± 8.63 (range, 65 to 90), and the mean Harris Hip Score collected at the final follow-up was 72.47 ± 8.85 (range, 60 to 86) (P < 0.05). The mean pre-trauma VAS was 2.20 ± 1.21 (range, 0 to 4), and the mean VAS recorded at the final follow-up was 3.00 ± 1.41 (range, 0 to 5) (P < 0.05). According to the EQ5D-DL assessed at the final follow-up, no patient felt that their daily life and activities became more problematic. CONCLUSION: This study provided added validation of the current management of periprosthetic femoral fractures after total hip arthroplasty. Using the proper fixation and implant can achieve a reliable fixation and good functional recovery. LEVEL OF EVIDENCE: IVa.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".