Comparison of cadaveric and isomorphic three‐dimensional printed models in temporal bone education
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: Current three-dimensional (3D) printed simulations are complicated by insufficient void spaces and inconsistent density. We describe a novel simulation with focus on internal anatomic fidelity and evaluate against template/identical cadaveric education. STUDY DESIGN: Research ethics board-approved prospective cohort study. METHODS: Generation of a 3D printed temporal bone was performed using a proprietary algorithm that deconstructs the digital model into slices prior to printing. This supplemental process facilitates removal of residual material from air-containing spaces and permits requisite infiltrative access to the all regions of the model. Ten otolaryngology trainees dissected a cadaveric temporal bone (CTB) followed by a matched/isomorphic 3D printed bone model (PBM), based on derivative micro-computed tomography data. Participants rated 1) physical characteristics, 2) specific anatomic constructs, 3) usefulness in skill development, and 4) perceived educational value. The survey instrument employed a seven-point Likert scale. RESULTS: Trainees felt physical characteristics of the PBM were quite similar to CTB, with highly ranked cortical (5.5 ± 1.5) and trabecular (5.2 ± 1.3) bone drill quality. The overall model was considered comparable to CTB (5.9 ± 0.74), with respectable air cell reproduction (6.1 ± 1.1). Internal constructs were rated as satisfactory (range, 4.9-6.2). The simulation was considered a beneficial training tool for all types of mastoidectomy (range, 5.9-6.6), posterior tympanotomy (6.5 ± 0.71), and skull base approaches (range, 6-6.5). Participants believed the model to be an effective training instrument (6.7 ± 0.68), which should be incorporated into the temporal bone lab (7.0 ± 0.0). The PBM was thought to improve confidence (6.7 ± 0.68) and operative performance (6.7 ± 0.48). CONCLUSIONS: Study participants found the PBM to be an effective platform that compared favorably to CTB. The model was considered a valuable adjunctive training tool with both realistic mechanical and visual character. LEVEL OF EVIDENCE: NA
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 | 0.004 | 0.011 |
| 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 | 0.001 | 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".