Optical Clearing Agents for Optical Imaging Through Cartilage Tympanoplasties: A Preclinical Feasibility Study
Bibliographic record
Abstract
HYPOTHESIS: Optical clearing agents (OCAs) can render cartilage tympanoplasty grafts sufficiently transparent to permit visualization of middle ear structures in an operated ear using optical coherence tomography (OCT) imaging. METHODS: Pieces of human tragal cartilage were treated with glycerol, a commonly used OCA. A reference reflector was imaged with OCT through the tympanoplasty as it cleared and the optical attenuation of the graft was measured. The reversibility of clearing and the dimensional changes associated with glycerol absorption were also measured. In a separate experiment, a human cadaveric temporal bone was prepared to simulate an ossiculoplasty surgery with cartilage replacement of the tympanic membrane. A partial ossicular replacement prosthesis (PORP) inserted in the ear was imaged with OCT through a 0.4mm cartilage graft optically cleared with glycerol. MAIN OUTCOME MEASURE: The optical attenuation of 0.4mm cartilage grafts decreased at 2.3+/-1.1 dB/min following treatment with glycerol, reaching a total decrease in attenuation of 13.6+/-5.9 dB after 7 minutes. The optical and dimensional effects of glycerol absorption were reversable following saline washout. In the temporal bone preparation, treatment of a cartilage graft with glycerol resulted in a 13 dB increase in signal-to-noise ratio and a 13 dB increase in contrast for visualizing the PORP through the graft with OCT. CONCLUSIONS: Optical clearing agents offer a potential pathway towards optical coherence tomography imaging of the middle ear in post-surgical ears with cartilage grafts.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".