Prosthetic reconstruction from the tympanic membrane to the stapes head or to the stapes footplate? A laser Doppler study.
Bibliographic record
Abstract
BACKGROUND: In the absence of the incus, many surgeons believe that reconstruction from the tympanic membrane to the stapes head is more effective than reconstruction to the stapes footplate. This has rarely been tested empirically. Published better clinical results with reconstruction to the stapes head might simply reflect less underlying disease in ears with an intact stapes superstructure. OBJECTIVE: To compare vibration transmission of these two forms of prosthetic reconstruction. METHODS: A fresh human cadaveric temporal bone model was used. Round window vibrations in response to sound in the ear canal were measured with a laser Doppler vibrometer. After incus removal, the discontinuity was repaired using a titanium prosthesis. Reconstruction from the tympanic membrane to the stapes head was compared to reconstruction to the stapes footplate. RESULTS: Reconstruction of both types decreased round window vibrations by 10 to 15 dB between 500 and 3000 Hz compared to the intact middle ear. Reconstruction to the stapes head performed 5 to 10 dB better at lower frequencies (500-2000 Hz), but this was only statistically significant at 1 and 2 kHz. CONCLUSIONS: There is only a 5 to 10 dB mechanical advantage gained by reconstruction from the tympanic membrane to the stapes head compared to reconstruction to the footplate for frequencies between 1 and 2 kHz.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".