Evaluation of Cochlear Duct Length Computations Using Synchrotron Radiation Phase-Contrast Imaging
Bibliographic record
Abstract
HYPOTHESIS: Evaluation of cochlear duct length (CDL) using novel imaging techniques will help improve the accuracy of existing CDL equations. BACKGROUND: Various relationships relating A value measured from a patient's computed tomography scan and CDL have been proposed to aid in preoperative electrode selection and frequency mapping. METHODS: Ten cadaveric temporal bones were scanned using synchrotron radiation phase-contrast imaging. Reference CDL values were calculated by placing points representing the organ of Corti (OC), lateral wall (LW), and electrode location (I) on the synchrotron radiation phase-contrast imaging slices along the length of the cochlea. The CDL estimates from the existing three equations (OC, LW, I) in addition to two newly proposed equations (OC and LW) were compared with reference CDL values at each respective location. RESULTS: When compared with reference CDL values, the new OC equation improved the CDL estimates from a 6.2% error to a 5.1% error while the new LW equation improved the CDL estimate error from 3.9 to 3.6%. Bland-Altman plots revealed both new equations increased similarity to reference values and brought more samples to within clinically significant ranges. Validation of the original electrode location equation to the reference values showed a 4.6% difference. CONCLUSION: The newly proposed equations for LW and OC provided an improvement over past equations for determining CDL from the A value by showing improved agreement with reference values. Therefore, these equations can provide quick and accurate preoperative estimates of CDL for improving customized frequency mapping.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".