Face Validity Study of an Artificial Temporal Bone for Simulation Surgery
Bibliographic record
Abstract
HYPOTHESIS: Using the rapid prototype (RP) technology, a physical construct of a human temporal bone was developed based on cadaveric tissue to permit simulated surgical training. The objective of the study was to test the face validity of the model. BACKGROUND: The cost and access to human cadaveric temporal bones is becoming increasingly challenging, particularly if there are religious and regulatory restrictions. There is a need to develop alternative strategies to improve accessibility. METHODS: Ultra high-resolution computed tomography (CT) images (0.15-mm resolution) were obtained from a cadaver temporal bone. Manual segmentation and conversion into a stereolithography file format permitted printing on a RP stereolithography printer. A 3-dimensional physical model was hardened to achieve the desired consistency. Eight practicing otologists were recruited to evaluate this model. Respondents were asked to drill the artificial bone and complete a rating survey upon completion. RESULTS: In using a Likert scale between 1 and 5, results for anatomic accuracy were favorable, with the best scores for overall morphology (4.63) and for lateral structures within the bone (4.5). The poorest scores were for the semicircular canals (3.75) and chorda tympani (3.25). Scores for haptic realism were good as well. The average score for the question "overall, how valuable is the model as a surgical simulator" was 4.1. The experts felt that junior residents (PGY 1-3) would benefit most from this surgical education model. CONCLUSION: The outer structures of the RP artificial temporal bone can be considered to have face validity. Improvements will continue to be made to address some of the deficiencies in the anatomic and haptic realism of this model.
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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 | 0.010 | 0.043 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".