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Record W2015255988 · doi:10.1016/j.otohns.2007.01.019

How does Prosthesis Head Size Affect Vibration Transmission in Ossiculoplasty?

2007· article· en· W2015255988 on OpenAlexaff
Manohar Bance, Alfredo Campos, Lillian Wong, David P. Morris, René Van Wijhe

Bibliographic record

VenueOtolaryngology · 2007
Typearticle
Languageen
FieldMedicine
TopicEar Surgery and Otitis Media
Canadian institutionsDalhousie University
Fundersnot available
KeywordsFootplateStapesIncusLaser Doppler vibrometerCadaveric spasmProsthesisTemporal boneMiddle earMedicineMaterials scienceAnatomySurgeryEngineering

Abstract

fetched live from OpenAlex

OBJECTIVES: The transmission of vibrations from the tympanic membrane to the stapes footplate by an ossicular reconstruction prosthesis is affected by the size of the prosthesis head. We sought to determine if augmenting or reducing the head size of prosthesis had a systematic effect on transmission of vibrations to the stapes. STUDY DESIGN: We conducted a fresh cadaveric temporal bone middle ear study. METHODS: The incus was replaced with a prosthesis using a tympanic membrane to stapes head (TASH)-type hydroxyapatite prosthesis in nine fresh cadaveric temporal bones. Three prosthesis head sizes were created: unaltered, reduced, and augmented. Stapes vibrations were measured with a laser Doppler vibrometer in response to acoustic frequency chirps at 90 dB SPL. RESULTS: All three head size prostheses resulted in smaller stapes vibrations than the intact ear. There was no difference in the vibration transmission between the three different head sizes. All prostheses showed a vibration loss of 10 to 15 dB compared to the intact ear. CONCLUSIONS AND SIGNIFICANCE: Within the range of sizes tested, prosthesis head size had little impact on vibration transmission to the stapes footplate.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.258
Teacher spread0.249 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations14
Published2007
Admission routes1
Has abstractyes

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