Biomechanical analysis of transverse acetabular fracture fixation in the elderly via the posterior versus the anterior approach with and without a total hip arthroplasty
Bibliographic record
Abstract
This study provides the first biomechanical comparison of the fixation constructs that can be created to treat transverse acetabular fractures when using the "gold-standard" posterior versus the anterior approach with and without a total hip arthroplasty in the elderly. Synthetic hemipelvises partially simulating osteoporosis (n = 24) were osteotomized to create a transverse acetabular fracture and then repaired using plates/screws, lag screws, and total hip arthroplasty acetabular components in one of four ways: posterior approach (n = 6), posterior approach plus a total hip arthroplasty acetabular component (n = 6), anterior approach (n = 6), and anterior approach plus a total hip arthroplasty acetabular component (n = 6). All specimens were biomechanically tested. No differences existed between groups for stiffness (range, 324.6-387.3 N/mm, p = 0.629), clinical failure load at 5 mm of femoral head displacement (range, 1630.1-2203.9 N, p = 0.072), or interfragmentary gapping (range, 0.67-1.33 mm, p = 0.359). Adding a total hip arthroplasty acetabular component increased ultimate mechanical failure load for posterior (2904.4 vs. 3652.3 N, p = 0.005) and anterior (3204.9 vs. 4396.0 N, p = 0.000) approaches. Adding a total hip arthroplasty acetabular component also substantially reduced interfragmentary sliding for posterior (3.08 vs. 0.50 mm, p = 0.002) and anterior (2.17 vs. 0.29 mm, p = 0.024) approaches. Consequently, the anterior approach with a total hip arthroplasty may provide the best biomechanical stability for elderly patients, since this fixation group had the highest mechanical failure load and least interfragmentary sliding, while providing equivalent stiffness, clinical failure load, and gapping compared to other surgical options.
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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 | 0.001 |
| 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".