Total Knee Arthroplasty With Robotic and Augmented Reality Guidance: A Hierarchical Task Analysis
Bibliographic record
Abstract
Background: Total knee arthroplasty (TKA) is a commonly performed procedure that has traditionally utilized reproducible steps using a set of mechanical instruments. The number of TKAs performed using robotic assistance is increasing, and augmented reality (AR) navigation systems are being developed. Hierarchical task analysis (HTA) aims to describe the steps of a specific task in detail to reduce errors and ensure reproducibility. The objective of this study was to develop and validate HTAs for conventional, robotic-assisted, and AR-navigated TKA. Methods: The development of HTAs for conventional TKA involved an iterative review process that incorporated the input of 4 experienced arthroplasty surgeons. The HTAs were then adapted for robotic-assisted and AR-navigated TKA by incorporating specific steps associated with the use of these systems. The accuracy and completeness of the HTAs were validated by observing 10 conventional and 10 robotic-assisted TKA procedures. Results: HTAs for conventional, robotic-assisted, and AR-navigated TKA were developed and validated. The resulting HTAs provide a comprehensive and standardized plan for each procedure and can aid in the identification of potential areas of inefficiency and risk. Robotic-assisted and AR-navigated approaches require additional steps, and there are an increased number of instances where complications may occur. Conclusions: The HTAs developed in this study can provide valuable insights into the potential pitfalls of robotic-assisted and AR-navigated TKA procedures. As AR-navigation systems are developed, they should be optimized by critical analysis using the developed HTAs to ensure maximum efficiency, reliability, accessibility, reduction of human error, and costs.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".