Effects of tensor tympani muscle contraction on the middle ear and markers of a contracted muscle
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: Many otologic disorders have been attributed to dysfunction of the tensor tympani muscle, including tinnitus, otalgia, Meniere's disease and sensorineural hearing loss. The objective of this study was to determine adequate stimuli for tensor tympani contraction in humans and determine markers of the hypercontracted state that could be used to detect this process in otologic disease. STUDY DESIGN: Multiple types of studies. METHODS: Studies included 1) measuring middle ear impedance changes in response to orbital puffs of air, facial stroking, and self-vocalization; 2) measuring changes in stapes and eardrum vibrations and middle ear acoustic impedance in response to force loading of the tensor tympani in fresh human cadaveric temporal bones; 3) measuring changes in acoustic impedance in two subjects who could voluntarily contract their tensor tympani, and performing an audiogram with the muscle contracted in one of these subjects; and 4) developing a lumped parameter computer model of the middle ear while simulating various levels of tensor tympani contraction. RESULTS: Orbital jets of air are the most effective stimuli for eliciting tensor tympani contraction. As markers for tensor tympani contraction, all investigations indicate that tensor tympani hypercontraction should result in a low-frequency hearing loss, predominantly conductive, with a decrease in middle ear compliance. CONCLUSIONS: These markers should be searched for in otologic pathology states where the tensor tympani is suspected of being hypercontracted.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".