Surgical Device Representatives Preference for Experiential Learning via Immersive Virtual Reality
Bibliographic record
Abstract
Background Surgical device representatives (SDRs) traditionally receive product training through didactic and hands-on learning. Immersive virtual reality (IVR) training effectiveness and user perceptions has not been studied in this population. The purpose of this study was to evaluate the learning efficiency of IVR for SDRs using a revision total knee arthroplasty system, determine SDR views on the use of IVR for training, and to perform a cost analysis of IVR training compared to traditional means. Methods A cross-sectional study of SDRs attending an anual sales meeting of a single orthopaedic implant company completed a revision total knee arthroplasty module using the Precision OS IVR System after a 1-hour traditional didactic lecture and small-group discussion training format. Immersive VR performance was tracked by the Precision OS software including repetitions, completion times, and performance measured using a previously validated performance metric, the Precision Score. An eight question survey was collected after IVR module completion. Results Two-hundred eighty-six SDRs completed over 1048 module repetitions for an average of approximately 4 module completions per SDR. The cumulative IVR training time was 18.3 minutes (SD 8.5). There was a high rate of critical injuries (n=458, 74%) measured including the medial and lateral collateral ligaments, popliteal artery, and amount of bony resection. The Precision Score had strong, inverse correlation to these injuries and were tracked over module repetitions. Two-hundred forty-five SDRs completed the post-training survery for a response rate of 86%. Most participants (91%) preferred IVR to standard training formats. Almost all participants (99%) intended to use their IVR learning in their current roles, and 99% felt the use of IVR was relevant to their roles. Similarly, 99% would recommend IVR training to others. The cumulative direct costs of IVR training compared to traditional didactic, in-person training formats for SDRs was 2.8x less costly, a lower bound as this did not account for lost productivity. Conclusion Immersive VR training is able to assess and track SDR learner performance and is valued by SDRs greater than traditional training formats. The cost of incorporating this technology is 2.8x less than current didactic and in-person learning formats. This is the first study of its kind to assess the use of IVR in the SDR population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".