What are the most difficult steps in total hip arthroplasty for early-career surgeons and how can future technology help most? A survey of the American Association of Hip and Knee Surgeons Young Arthroplasty Group
Bibliographic record
Abstract
While technology in total hip arthroplasty (THA) is generally developed by experienced surgeons, particular difficulties in surgical steps and technical demands experienced by early-career surgeons remain important. This study investigated the challenges early-career surgeons currently face in THA in order to obtain insights into how they perceive future innovative technologies might reduce the difficulties and complications associated with THA. Members of the American Association of Hip and Knee Surgeons Young Arthroplasty Group were surveyed. The perceived degree of difficulty with key THA surgical steps and the utility of adjunctive technology were recorded (scale 1–10, from no difficulty/not important to very difficult/very important). The membership response rate was 16.1%. The most common THA approach was the direct anterior approach (DAA) (67.4%). Four of the top five most difficult surgical steps were associated with cup preparation/placement. Fluoroscopy was the most used technology (56.7%), followed by no technology use (21.2%) and robot arm assistance (12.1%). With respect to the importance of technology, accurate cup placement ranked first. Dislocation and stem subsidence were the top two complications for which technological advances were thought to be most beneficial. Subgroup analysis based on surgical approach suggested that DAA users had significantly more difficulty with direction of stem rasping than non-DAA users ( p < 0.0001). This is the first survey to reveal data about perceived difficulties in THA surgical steps from the perspective of early-career arthroplasty surgeons and the role of technology in mitigating these complications. Future technological developments may take these needs of early-career surgeons into account. • Survey focused on difficulties of and technology hope for total hip arthroplasty. • Fluoroscopy was the most preferred technology, especially for direct anterior approach users. • Cup preparation/placement were considered the most difficult surgical steps. • Technological advances were most expected for dislocation and stem subsidence.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| 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".