Face, Content, Construct and Convergent Validity of a Surgical Spine Simulator for Pedicle Screw Insertions
Bibliographic record
Abstract
BACKGROUND: Spine simulators offer learners risk-free environments to develop psychomotor skills for pedicle screw insertions. The virtual reality TSYM simulator deconstructs and simulates pedicle screw insertions. This case series study investigates face, content, construct, and convergent validity of an L4-L5 bilateral pedicle screw insertion on the TSYM simulator. METHODS: Neurosurgical-orthopedic residents, fellows, and spine surgeons performed an L4-L5 bilateral pedicle screw insertion on the TSYM simulator. Participants were classified a priori into skilled (postgraduate year (PGY) 5-6, fellows, and consultant neurosurgeons or orthopedic surgeons) or less skilled (PGY 1-4) groups. Face and content validity were assessed utilizing a 7-point Likert scale. Construct validity was determined by investigating group differences in simulation-derived performance metrics and the Objective Structured Assessment of Technical Skills (OSATS) ratings. Convergent validity was examined by correlating simulation-derived performance metrics and OSATS ratings. RESULTS: < 0.001). Three simulation-derived performance metrics (maximum force and tool contact using the simulated screwdriver and three-dimensional velocity using the tap) significantly correlated with OSATS ratings. CONCLUSION: The L4-L5 bilateral pedicle screw insertion simulation on the TSYM platform demonstrated mixed and variable evidence for face, content, construct and convergent validity, supporting its educational potential for spine surgery training, but improvements are needed to optimize learning.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".