High-frequency ultrasound Doppler velocimetry measurements of intra-cochlear structures in human temporal bones.
Bibliographic record
Abstract
Hearing loss is one of the most prevalent, chronic, and fast growing disorders, affecting approximately one-tenth of the population. Diagnostic imaging tools currently used in this field such as CT and MRI do not have sufficient spatial/temporal resolution to properly diagnose most of the underlying causes of hearing loss. In this work, we present the first high-resolution velocimetry measurements of intra-cochlear structures using high-frequency (40 MHz) pulsed-wave Doppler ultrasound. A 40-MHz single-element transducer based on PMN-PT single-crystal piezoelectric was fabricated in-house and mounted onto the tip of a needle. The transducer was a side-looking circular disk with a 1 mm outer diameter. A custom data acquisition system was developed in order to perform pulsed-wave Doppler and was synchronized with an acoustic stimulus. Velocimetry measurements were performed on the basilar membrane located inside the cochlea of fresh temporal bones by imaging across the round window membrane and stimulating the eardrum with pure-tone sounds. For the first time, the vibration of the basilar membrane was measured on a completely intact cochlea using ultrasound velocimetry. The middle ear resonance could be detected by measuring the frequency-dependent basilar membrane velocities between 100 and 2500 Hz. This resonance was verified using a laser vibrometer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".