Surgical Approaches in Total Hip Arthroplasty Cost Per Case Analysis: A Retrospective, Matched, Micro-costing Analysis in a Socialised Healthcare System
Bibliographic record
Abstract
Background: Total hip arthroplasty (THA) offers an effective method of pain relief and restoration of function for patients with end-stage arthritis. The anterior approach (AA) claims to benefit patients with decreased pain, increased mobilisation and decreasing length of hospital stay (LOS). In a socialised healthcare platform we questioned whether the AA, compared to posterior (PA) and lateral (LA) approaches, can decrease the cost burden. Methods: Using a retrospective matched cohort study, we matched 69 AA patients to 69 LA and 69 PA patients for age ( p = 0.99), gender ( p = 0.99) and number of pre-surgical risk factors ( p = 0.99). First, we used the Resource Intensity Weights (RIW) using the Health Services agreed on method of calculating cost. Secondly, micro-costing analysis was performed using the financial services data for each patient’s hospital stay. Results: Using the RIW based cost analysis and 2-day reduction (95% CI 1.8–2.4) in LOS, the AA offers an estimated savings per case of $4099 ( p < 0.001) compared to the LA and PA. Using micro-costing analysis, we found a total saving of $1858.00 per case (95% CI 1391–2324) when comparing the AA to the PA and LA. There was a statistically significant cost savings using every category: Net Direct Salary ($901.00, p < 0.001), Net Drug ($8.00, p = 0.003), Patient Supply ($454.00, p = 0.001), Patient Drug ($15.00, p = 0.008), Indirect Cost ($385.00, p < 0.001), Patient Care Administration ($106.00, p < 0.001). Furthermore, the AA saved 142 minutes of in-hospital rehabilitation time. Conclusion: The AA THA provides statistically significant reductions in cost compared to PA and LA while releasing rehabilitation resources.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".