Auditory effects of autologous fat graft for TORP stabilization in the middle ear: A cadaveric study
Bibliographic record
Abstract
BACKGROUND: Total ossicular replacement prostheses (TORP) are often used to re-establish ossicular coupling of sound in an ear lacking a stapes supra-structure. The use of TORPs, however, is associated with a 2/3 five year failure rate due to their anatomic instability over time in the middle ear. The use of autologous fat to try and stabilize TORPs may improve long-term results with this challenging ossicular reconstruction technique. METHODS: A cadaveric temporal bone model was developed and laser Doppler vibrometry was used to measure and record round window membrane vibration in response to sound stimulation under the following conditions: normal middle ear, middle ear filled with fat, normal middle ear with TORP prosthesis, TORP prosthesis with fat around its distal end and TORP prosthesis with fat filling the middle ear. Fourteen temporal bones were used. RESULTS: There was a significant decrease in round window membrane velocity after filling the middle ear with fat in both the normal middle ear (- 8.6 dB; p < 0.0001) and prosthesis conditions (- 13.7 dB; p < 0.0001). However, there was no significant drop in round window membrane velocity associated with using fat around the distal end of the TORP prosthesis as compared to the prosthesis without fat condition (p > 0.05). CONCLUSIONS: Autologous fat around the distal end of a TORP prosthesis may not be associated with any additional hearing loss, as demonstrated in this cadaveric model. The additional hearing loss potentially caused by using fat to completely surround the prosthesis and fill the middle ear is probably not clinically acceptable at this time, especially given the unknown way in which the fat will atrophy over time in this context.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".