High Variability in the Use of Cement for Femoral Stem Fixation in Hip Fractures—An Analysis of the Canadian Joint Replacement Registry
Bibliographic record
Abstract
Background: This study examines current trends in Canada using data from the Canadian Joint Replacement Registry (CJRR) and includes a national survey to understand the varied uptake of cement for femoral stem fixation. Methods: The survey was available online and the website link was distributed to all orthopaedic surgeons through the Canadian Orthopaedic Association between September and December 2022. The CJRR obtained data from the Canadian Institute for Health Information (CIHI), and information pertaining to patients 55 years of age and older who underwent hemiarthroplasty for hip fracture in Canada between April 2017 and March 2022 was used. Results: Most respondents practiced in an academic community setting (52%). Only 53% of respondents reported using cement, and 71% indicated that cemented fixation was the best practice. The main reasons for using uncemented stems were less operative time (23%), cement disease concerns (11%), and surgeons’ comfort (10%). Similarly, CJRR data showed only 51% cemented fixation among 42,386 hemiarthroplasties performed between 2017 and 2022. The proportion of cemented implants varied by province, but overall, the increase in the use of cement from 2017 to 2022 was from 42.9% to 57.7%. Conclusions: This study demonstrates variability in the use of cement for femoral fixation despite solid evidence showing improved outcomes using cement. Some of the main reasons in favour of uncemented stems include operative time, surgical training, and concerns about cement disease. Establishing clear position statements and guidelines supporting cemented fixation may be prudent to build universal consensus on this practice.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".