A Multicenter Randomized Controlled Trial Evaluating the Effectiveness of Cognitive Training for Anterior Approach Total Hip Arthroplasty
Bibliographic record
Abstract
BACKGROUND: For total hip arthroplasty (THA), cognitive training prior to performing real surgery may be an effective adjunct alongside simulation to shorten the learning curve. This study sought to create a cognitive training tool (CTT) to perform anterior approach (AA)-THA, which was validated by expert surgeons, and test its use as a training tool compared with conventional material. METHODS: We employed a modified Delphi method with 4 expert surgeons from 3 international centers of excellence. Surgeons were independently observed performing THA before undergoing semistructured cognitive task analysis (CTA) and before completing successive rounds of surveys until a consensus was reached. Thirty-six surgical residents (postgraduate year [PGY]-1 through PGY-4) were randomized to cognitive training or training with a standard operation manual with surgical videos before performing a simulated AA-THA. RESULTS: The consensus CTA defined THA in 11 phases, in which were embedded 46 basic steps, 36 decision points, and 42 critical errors and linked strategies. This CTA was mapped onto an open-access web-based CTT. Surgeons who prepared with the CTT performed a simulated THA 35% more quickly (time, mean 28 versus 38 minutes) with 69% fewer errors in instrument selection (mean 29 versus 49 instances), and required 92% fewer prompts (mean 13 versus 25 instances). They were more accurate in acetabular cup orientation (inclination error, mean 8° versus 10°; anteversion error, mean 14° versus 22°). CONCLUSIONS: This validated CTT for arthroplasty provides structure for competency-based learning. It is more effective at preparing orthopaedic trainees for a complex procedure than conventional materials, as well as for learning sequence, instrumentation utilization, and motor skills. CLINICAL RELEVANCE: Cognitive training combines education on decision-making, knowledge, and technical skill. It is an inexpensive technique to teach surgeons to perform hip arthroplasty and is more effective than current preparation methods.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".