Clinical Applications of a Finite-Element Model of the Human Middle Ear
Bibliographic record
Abstract
Computer-generated models are increasingly being used in otolaryngology for teaching purposes, preoperative planning, and clinical simulations, especially when dealing with small, complex areas such as the middle ear. One technique used to analyze the mechanics of complex models is the finite-element method, whereby the system of interest is divided into a large number of small, simple elements. The mechanical properties and applied forces are represented by functions defined over each element, and the mechanical response of the whole system can then be computed. We present a unique three-dimensional finite-element model of the human eardrum and middle ear. Our model takes advantage of phase-shift moiré shape measurements to precisely define the shape of the eardrum. The middle ear geometry is derived from histologic serial sections and from high-resolution magnetic resonance microscopy of the human ear. We discuss the importance of this model in terms of understanding and teaching the mechanics of the human middle ear, simulating various pathologic conditions, and designing ossicular prostheses.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".