Total hip arthroplasty versus hemiarthroplasty for treatment of femoral neck fractures
Bibliographic record
Abstract
AIMS: This study aimed to describe practice variation in the use of total hip arthroplasty (THA) for older patients with femoral neck fracture and to determine the association between patient, surgeon, and institution factors and treatment with THA. METHODS: We performed a cross-sectional analysis of 49,597 patients aged 60 years and older from Ontario, Canada, who underwent hemiarthroplasty or THA for femoral neck fracture between 2002 and 2017. This population-based study used routinely collected healthcare databases linked through ICES (formerly known as the Institute for Clinical Evaluative Sciences). Multilevel logistic regression modelling was used to quantify the association between patient, surgeon, and institution-level variables and whether patients were treated with THA. Variance partition coefficient and median odds ratios were used to estimate the variation attributable to higher-level variables and the magnitude of effect of higher-level variables, respectively. RESULTS: Over the study period, 9.4% of patients (n = 4,638) were treated with THA. Patient factors associated with higher likelihood of treatment by THA included: younger age, male sex, and diagnosis with rheumatoid arthritis. Long-term care residence, use of home care services prior to hip fracture, diagnosis of dementia, higher comorbidity burden, and the most marginalized group were negatively associated with treatment by THA. Treating surgeon and institution accounted for 54.2% and 17.8% of the total variation in treatment with THA, respectively. Surgeon volume of THA procedures in the 365 days prior to surgery was the strongest higher-level predictor of treatment with THA. Specific treating surgeons and institutions still accounted for significant proportions of the variability in treatment with THA (40.3% and 19.5% of total observed variation, respectively) after controlling for available patient, surgeon, and institution-level variables. CONCLUSION: 2023;105-B(2):180-189.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".