Trends and patient factors associated with technology-assisted total hip arthroplasty in the United States from 2005 to 2014
Bibliographic record
Abstract
Background Technology-assisted total hip arthroplasty (TA-THA) using either computer-assisted navigation or robotic assistance has become increasingly more popular. The purpose of this study was to examine the trends and patient factors associated with TA-THA. Methods This is a retrospective review utilizing the National Inpatient Sample, a large national database incorporating inpatient hospitalization information. International Classification of Diseases, 9th Revision codes were used to identify patients with hip osteoarthritis who underwent primary total hip arthroplasty (THA). Patients were then separated into those who underwent TA-THA or conventional THA. Outcomes of interest included annual TA-THA utilization; patient and hospital characteristics associated with TA-THA; and trends for length of stay (LOS), cost, and discharge to home. Results From 2005 to 2014, a total of 2,588,304 patients with hip osteoarthritis who underwent THA were identified in the National Inpatient Sample database. Of those, 39,700 (1.5%) underwent TA-THA. The number of TA-THA procedures increased from 178 (0.1% of all THA) in 2005 to 10,045 (3.0% of all THA) in 2014, which represented a 30-fold increase in incidence ( P -trend <.0001). TA-THA was associated with Hispanic race, higher patient income, and the Western region of the United States. During the study period, there was a trend toward decreased LOS and increased discharge to home for both TA-THA and conventional THA. TA-THA was associated with higher inpatient cost. Conclusion TA-THA is being increasingly used in the United States and is associated with specific patient factors. However, the value of TA-THA compared to conventional THA remains unclear and should be assessed with future research. Level of Evidence III (retrospective cohort study).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".