The cost of hemiarthroplasty compared to that of internal fixation for femoral neck fractures
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: There is very little information on the cost of different treatments for femoral neck fractures. We assessed whether total hospital and societal costs of treatment of elderly patients with displaced femoral neck fractures differ between patients operated with internal fixation or hemiarthroplasty. METHODS: 222 patients (mean age 83 years, 165 women (74%)) who had been randomized to internal fixation or hemiarthroplasty were followed for 2 years. Resource use in hospital, rehabilitation, community-based care, and nursing home use were identified, quantified, evaluated, and analyzed. RESULTS: The average cost per patient for the initial hospital stay was lower for patients in the internal fixation group than in the hemiarthroplasty group (9,044 euro vs. 11,887 euro, p < 0.01). When all hospital costs, i.e. rehabilitation, reoperations, and formal and informal contact with the hospital were included, the costs were similar (21,709 euro for internal fixation vs. 19,976 euro for hemiarthroplasty). When all costs were included (hospital admissions, cost of nursing home, and community-based care), internal fixation was the most expensive treatment (47,186 euro vs. 38,615 euro (p = 0.09)). INTERPRETATION: The initial lower average cost per patient for internal fixation as treatment for a femoral neck fracture cannot be used as an argument in favor of this treatment, since the average cost per patient is more than outweighed by subsequent costs, mainly due to a higher reoperation rate after internal fixation.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".