Techniques and Technologies for the Intraoperative Assessment of Component Positioning, Leg Lengths, and Offset in Total Hip Arthroplasty: A Systematic Review
Bibliographic record
Abstract
Background: Many techniques and technologies exist for the intraoperative assessment of component positioning, leg lengths, and offset in total hip arthroplasty, but with limited comparative data. We conducted a systematic review of the available literature to evaluate the range of techniques and technologies for the intraoperative assessment of component position as well as leg lengths and offset in terms of accuracy, precision, surgical time, cost, and relationship to clinical outcomes. Methods: A comprehensive search of the Embase and Medline databases from 1974 to 2023 was performed. We included controlled or comparative prospective clinical studies. Cochrane's risk-of-bias tool for randomized trials and Risk of Bias in Non-Randomized Studies - of Interventions tools were used by 2 independent reviewers to evaluate each study for risk of bias. We conducted both qualitative and quantitative analyses of the studies included. However, a meta-analysis was deemed not to be feasible due to heterogeneity. Results: Our review included 25 studies with 52 intraoperative techniques and technologies. Mechanical guides and computerized navigation were most frequently evaluated in the included studies. Computerized navigation systems consistently showed the greatest accuracy and precision across all measures, at the cost of longer mean operative time. In contrast, freehand techniques demonstrated the poorest accuracy and precision. Insufficient data were found to reach any meaningful conclusions in terms of differences in overall surgical cost or clinical outcomes. Conclusions: Evidence shows that computerized navigation systems are most accurate and precise in positioning components during total hip arthroplasty. Further research is needed to determine their health and economic impact and whether the accuracy and precision of navigated techniques are justified in terms of clinical outcomes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".