Risk factors for post-operative periprosthetic fractures following primary total hip arthroplasty with a proximally coated double-tapered cementless femoral component
Bibliographic record
Abstract
Aims The aim of this study was to identify patient- and surgery-related risk factors for sustaining an early periprosthetic fracture following primary total hip arthroplasty (THA) performed using a double-tapered cementless femoral component (Bi-Metric femoral stem; Biomet Inc., Warsaw, Indiana). Patients and Methods A total of 1598 consecutive hips, in 1441 patients receiving primary THA between January 2010 and June 2015, were retrospectively identified. Level of pre-operative osteoarthritis, femoral Dorr type and cortical index were recorded. Varus/valgus placement of the stem and canal fill ratio were recorded post-operatively. Periprosthetic fractures were identified and classified according to the Vancouver classification. Regression analysis was performed to identify risk factors for early periprosthetic fracture. Results The mean follow-up was 713 days (1 to 2058). A total of 48 periprosthetic fractures (3.0%) were identified during the follow-up and median time until fracture was 16 days, (interquartile range 10 to 31.5). Patients with femoral Dorr type C had a 5.2 times increased risk of post-operative periprosthetic fracture compared with type B, while female patients had a near significant two times increased risk over time for post-operative fracture. Conclusion Dorr type C is an independent risk factor for early periprosthetic fracture, following THA using a double tapered cementless stem such as the Bi-Metric. Surgeons should take bone morphology into consideration when planning for primary THA and consider using cemented femoral components in female patients with poor bone quality. Cite this article: Bone Joint J 2017;99-B:451–7.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".