High-Fidelity Microsurgical Simulation: The Thiel Cadaveric Nerve Model and Evaluation Instrument
Bibliographic record
Abstract
With surgical education moving from a time-based to a competency-based model, developing high-fidelity simulation models has become a priority. The Thiel cadaveric model has previously been used for a number of medical and surgical simulations, including microvascular simulation. We aim to investigate the use of the Thiel model in peripheral nerve simulation and validate a novel evaluation instrument. Sixteen residents ranging from postgraduate years 1 to 6 participated in the study. Their nerve coaptations using Thiel cadaveric nerves were video recorded and evaluated by 5 fellowship-trained microsurgeons using the Micro-Neurorrhaphy Evaluation Scale (MNES). The intraclass correlation among the 5 evaluators was 0.75, revealing excellent interrater reliability. The Cronbach α was .77, underlining the internal consistency of the test items. Bivariate analysis revealed a significant association between the MNES scores and the participants' self-declared level of experience. This correlation was confirmed by mixed modeling. Our results validate the MNES and underscore the utility of the Thiel nerve tissue for peripheral nerve surgical simulation.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".