Acute Periprosthetic Hip Fractures With Short, Uncemented Femoral Stems
Bibliographic record
Abstract
BACKGROUND: Uncemented femoral stems in hip arthroplasty have shown excellent long-term results, and several systematic studies indicate satisfactory performance of short stems. However, biomechanical and finite element analysis studies have suggested that shorter stems allow greater micromotion, producing greater strain at the implant-bone interface, which potentially increases the risk for periprosthetic fracture (PPF). We sought to assess this risk within our unit. METHODS: Our institution's arthroplasty database was searched for all primary total hip arthroplasties using short femoral stems performed between July 14, 2009 and August 29, 2022. The overall PPF rate and the PPF rate for individual femoral stems were established. Preoperative X-rays for each case were analyzed to characterize individual proximal femoral geometry. A data analysis was performed to identify risk factors for PPF. RESULTS: For the time period assessed, 3,192 short femoral stems were implanted. This included 1,561 of stem A and 1,631 of stem B. Women constituted 55.37% of the cohort. The average patient age was 66 years (range, 22 to 95). The PPF rate was 0.6%, with 19 PPFs identified at a follow-up of 3 months. There was a significantly higher fracture rate in stem A (0.96%) compared to stem B (0.25%) (P ≤ .01). Proximal femoral geometry, age, and sex were not determined to be risk factors for PPF in our cohort. Individual surgeons and surgical approaches appeared to confer no increased risk. There was no significant difference in average stem length, but multivariate analysis identified stem type and stem length as an independent risk factor for PPF. CONCLUSIONS: Our study identified individual stem and stem length as independent risk factors for PPF within our cohort. PPF is a multifactorial issue, and consensus on emerging risk factors such as implant design will hopefully inform decisions that can provide further risk reduction for individual patients.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".