Evaluation of Cochlear Duct Length Measurements From a 3D Analytical Cochlear Model Using Synchrotron Radiation Phase-Contrast Imaging
Bibliographic record
Abstract
HYPOTHESIS: Evaluating the accuracy of cochlear duct length (CDL) measurements from a published three-dimensional (3D) analytical cochlear model using Synchrotron Radiation Phase-Contrast Imaging (SR-PCI) data will help determine its clinical applicability and allow for model adjustments to increase accuracy. BACKGROUND: Accurate CDL determination can aid in cochlear implant sizing for full coverage and frequency map programming, which has the potential to improve hearing outcomes in patients. To overcome problems with the currently available techniques for CDL determination, a novel 3D analytical cochlear model, dependent on four basal turn distances, was proposed in the literature. METHODS: SR-PCI data from 11 cadaveric human cochleae were used to obtain reference measurements. CDL values generated by the analytical cochlear model were evaluated in two conditions: when the number of cochlear turns (NCT) were automatically predicted based on the four input distances, and when the NCT were manually specified based on SR-PCI data. RESULTS: When the analytical cochlear model automatically predicted the NCT, the mean absolute error was 2.6 ± 1.6 mm, with only 27% (3/11) of the samples having an error in the clinically acceptable range of ±1.5 mm. When the NCT were manually specified based on SR-PCI data, the mean absolute error was reduced to 1.0 ± 0.6 mm, with 73% (8/11) of the samples having a clinically acceptable error. CONCLUSION: The 3D analytical cochlear model introduced in the literature is effective at modeling the 3D geometry of individual cochleae, however tuning in the NCT estimation is required.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".