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Record W2086754189 · doi:10.2310/7070.2001.19393

Clinical Applications of a Finite-Element Model of the Human Middle Ear

2001· article· en· W2086754189 on OpenAlexaffvenue
Sam J. Daniel, W. Robert J. Funnell, Anthony Zeitouni, Melvin D. Schloss, Jamie M. Rappaport

Bibliographic record

VenueThe Journal of Otolaryngology · 2001
Typearticle
Languageen
FieldMedicine
TopicEar Surgery and Otitis Media
Canadian institutionsMcGill University
Fundersnot available
KeywordsEardrumMiddle earFinite element methodHuman earSimple (philosophy)MedicineAcousticsComputer scienceAnatomyPhysicsStructural engineeringEngineering

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.175

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.068
GPT teacher head0.334
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2001
Admission routes2
Has abstractyes

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