Variations in the use of cemented implants for hip fracture repair in Nova Scotia, Canada
Bibliographic record
Abstract
Background: Opinions differ on the use of a cemented versus uncemented implant for repair of femoral neck fractures. The purpose of this study was to compare variation in the use of cemented implants for patients with hip fracture in Nova Scotia, Canada. Methods: The study population was all patients who underwent primary emergency hip fracture arthroplasty in Nova Scotia between 2010/11 and 2019/20. We calculated 3 aggregate measures of variation across all hospitals: the extremal quotient (EQ) (ratio of the highest to the lowest rate of variation), the weighted coefficient of variation (WCV) (standard deviation divided by mean) and the systematic component of variation (SCV) (the between-surgeon variation, excluding the random component). Bootstrapped 95% confidence intervals (CIs) were computed. Results: Our study population included 3787 patients with hip fracture who underwent arthroplasty at 5 hospitals, of whom 2219 (58.6%, 95% CI 57.1%–60.2%) received cemented implants. The age- and sex-adjusted proportion of cemented cases ranged from 36.6% (95% CI 33.6%–40.0%) to 71.1% (95% CI 68.4%–73.8%). The highest EQ value was 30.3 (95% CI 0.0–80.0). The WCV ranged from 32.6 (95% CI 28.5–36.7) to 77.3 (95% CI 68.3–86.3). The SCV indicated very high between-surgeon variation at all hospitals. The WCV and SCV resulted in the same rankings for all hospitals, indicating consistency between the 2 measures. Conclusion: We found considerable variation across surgeons in the use of cemented implants for patients with hip fracture in Nova Scotia. Guidelines could help build consensus on this practice among surgeons.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".