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Record W1530153961 · doi:10.2310/7070.2010.090078

Simple mass loading of the tympanic membrane to alleviate symptoms of patulous eustachian tube.

2010· article· en· W1530153961 on OpenAlexaff
Clark Bartlett, Ronald J. E. Pennings, Allan Ho, David Kirkpatrick, René Van Wijhe, Manohar Bance

Bibliographic record

VenuePubMed · 2010
Typearticle
Languageen
FieldMedicine
TopicEar Surgery and Otitis Media
Canadian institutionsDalhousie University
Fundersnot available
KeywordsEardrumEustachian tubeMedicineAudiologyPopulationSurgeryMiddle earRadiology

Abstract

fetched live from OpenAlex

BACKGROUND: Patulous eustachian tube (PET) has a major impact on a patient's quality of life. The purpose of this study was to understand mechanisms behind the symptoms, develop treatments based on these, and develop and use a questionnaire to measure changes in PET symptoms with a novel intervention. Our hypothesis is that PET symptoms can be addressed at the level of the eardrum more easily than at the level of the eustachian tube. METHODS: In a population of 14 PET subjects and 6 fresh temporal bones, several investigations were performed. Nasal audiometry was used to measure frequencies preferentially transmitted to the ear in PET subjects. An intervention consisting of mass loading of the eardrum was devised in the temporal bones to damp these frequencies. This was then applied to subjects with PET. A questionnaire was developed and administered to measure the response to this intervention. This questionnaire included the more common symptoms associated with PET, such as echoing sounds, increased environmental sounds, and a plugging sensation in the ear. Mass loading of the eardrum was performed with Blu Tack, a clay-like, nontoxic substance. RESULTS/CONCLUSION: Low frequencies are preferentially transmitted in PET, and eardrum vibrations to these can be mitigated with mass loading. Mass loading in human subjects significantly reduced major symptoms of PET, although temporarily.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.141
Threshold uncertainty score0.319

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.011
GPT teacher head0.218
Teacher spread0.206 · 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

Citations26
Published2010
Admission routes1
Has abstractyes

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