“Can You Feel It”: An Early Experience with Simulated Vibration to Recreate Glenoid Reaming
Bibliographic record
Abstract
When developing educational simulators, meaningful haptic feedback is important. To our knowledge, no shoulder arthroplasty surgical simulator exists. This study focuses on simulating vibration haptics of glenoid reaming for shoulder arthroplasty using a novel glenoid reaming simulator. Methods: We validated a novel custom simulator constructed using a vibration transducer transmitting simulated reaming vibrations to a powered nonwearing reamer tip through a 3D-printed glenoid. Validation and system fidelity were evaluated by 9 fellowship-trained shoulder surgeon experts performing a series of simulated reamings. We then completed the validation process through a questionnaire focused on experts' experience with the simulator. Results: Experts correctly identified 52% ± 8% of surface profiles and 69% ± 21% of cartilage layers. Experts identified the vibration interface between simulated cartilage and subchondral bone (77% ± 23% of the time), indicating high fidelity for the system. An interclass correlation coefficient for experts' reaming to the subchondral plate was 0.682 (confidence interval 0.262-0.908). On a general questionnaire, the perceived utility of the simulator as a teaching tool was highly ranked (4/5), and experts scored "ease of instrument manipulation" (4.19/5) and "realism of the simulator" (4.11/5) the highest. The mean global evaluation score was 6.8/10 (range 5-10). Conclusions: We examined a simulated glenoid reamer and feasibility of haptic vibrational feedback for training. Experts validated simulated vibration feedback for glenoid simulation reaming, and the results suggested that this may be a useful additional training adjuvant. Level of Evidence: Level II, prospective study.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".