Effects of Ossicular Prosthesis Mass and Section of the Stapes Tendon on Middle Ear Transmission
Bibliographic record
Abstract
BACKGROUND: The effects of changing prosthesis mass on middle ear transmission have not been previously systematically studied. Neither has the effect of stapes tendon sectioning. These are important parameters that can be surgically varied. HYPOTHESIS: Because the middle ear is compliance dominated at low frequencies, prosthesis mass will affect transmission of higher frequencies in the middle ear. METHODS: Eight fresh cadaveric temporal bones, with the incus removed, were loaded with a replacement prosthesis from the tympanic membrane to the stapes head. Laser Doppler vibrometry was used to measure stapes footplate vibrations. Vibrations were measured in response to chirps from 250 to 8 kHz at 90 dB SPL in the ear canal. The unloaded prosthesis mass was approximately 16 mg. Loadings with masses of approximately 2, 12, and 30 mg were placed on the stem. Recordings were repeated after cutting the stapes tendon. RESULTS: Mass loading affected the higher frequencies only, with significant effects only above 4 kHz. There was little low-frequency effect. Stapes tendon section showed an improvement in the lower frequencies but did not reach statistical significance. CONCLUSIONS: Mass of prostheses affects mainly higher frequencies. There is no drop in lower frequencies from using lower masses, so lighter-mass prostheses may be preferred. Stapes tendon section does not have a detrimental effect on middle ear transmission after ossiculoplasty.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.004 | 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".