Trainee Surgeons Affect Operative Time but not Outcome in Minimally Invasive Total Hip Arthroplasty
Bibliographic record
Abstract
Training of young surgeons in total hip arthroplasty (THA) is crucial, but might affect operative time and outcome especially in minimally invasive (MIS) THA. We asked whether the learning curve of orthopaedic residents trained on MIS THA has an impact on (1) operative time (2) complication rates and (3) early postoperative outcome. In a retrospective analysis of over 1000 MIS THAs from our institutional joint registry, operative time, complication rates, patient reported outcome measures (Western Ontario and McMaster Universities Arthritis Index [WOMAC] and Euro-Qol 5D-5L [EQ-5D]) within the first year and responder rates for positive outcome as defined by the Outcome Measures in Rheumatology and Osteoarthritis Research Society International consensus responder (OMERACT-OARSI) criteria were compared between trainee and senior surgeons. Mean operative time was nine minutes longer for trainees compared to senior surgeons (78.1 ± 25.4 min versus 69.3 ± 23.8 min, p < 0.001). Dislocation (p = 0.21), intraoperative fracture (p = 0.84) and infection rates (p = 0.58) were comparably low in both groups. Both trainee and senior THAs showed excellent improvement of EQ-5D (0.34 ± 0.26 versus 0.32 ± 0.23, p = 0.40) and WOMAC (45.9 ± 22.1 versus 44.9 ± 20.0, p = 0.51) within the first year after surgery without clinical relevant differences. Similarly, responder rates for positive outcome were comparable between trainees with 92.9% and senior surgeons with 95.2% (p = 0.17). MIS THA seems to be a safe procedure during the learning curve of young orthopaedic specialists, but requires higher operative time.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".