Exploring the Role of 3-Dimensional Simulation in Surgical Training
Bibliographic record
Abstract
STUDY DESIGN: Randomized control study assessing the efficacy of a pedicle screw insertion simulator. OBJECTIVES: To evaluate the efficacy of an in-house developed 3-dimensional software simulation tool for teaching pedicle screw insertion, to gather feedback about the utility of the simulator, and to help identify the context and role such simulation has in surgical education. SUMMARY OF BACKGROUND DATA: Traditional instruction for pedicle screw insertion technique consists of didactic teaching and limited hands-on training on artificial or cadaveric models before guided supervision within the operating room. Three-dimensional computer simulation can provide a valuable tool for practicing challenging surgical procedures; however, its potential lies in its effective integration into student learning. METHODS: Surgical residents were recruited from 2 sequential years of a spine surgery course. Patient and control groups both received standard training on pedicle screw insertion. The patient group received an additional 1-hour session of training on the simulator using a CT-based 3-dimensional model of their assigned cadaver's spine. Qualitative feedback about the simulator was gathered from the trainees, fellows, and staff surgeons, and all pedicles screws physically inserted into the cadavers during the courses were evaluated through CT. RESULTS: A total of 185 thoracic and lumbar pedicle screws were inserted by 37 trainees. Eighty-two percent of the 28 trainees who responded to the questionnaire and all fellows and staff surgeons felt the simulator to be a beneficial educational tool. However, the 1-hour training session did not yield improved performance in screw placement. CONCLUSIONS: A 3-dimensional computer-based simulation for pedicle screw insertion was integrated into a cadaveric spine surgery instructional course. Overall, the tool was positively regarded by the trainees, fellows, and staff surgeons. However, the limited training with the simulator did not translate into widespread comfort with its operation or into improvement in physical screw placement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".