High-resolution cone-beam computed tomography: a potential tool to improve atraumatic electrode design and position
Bibliographic record
Abstract
CONCLUSIONS: Flat-panel cone-beam computed tomography (CBCT) is able to assess the trajectory of the implanted cochlear implant (CI) array. This is essential to determine specific effects of electrode design and surgical innovations on outcomes in cochlear implantation. CBCT is a non-invasive approach yielding similar data to histopathological analyses, with encouraging potential for use in surgical, clinical and research settings. OBJECTIVES: To examine the fidelity of CBCT imaging and custom 3D visualization in characterizing CI insertion in comparison to gold standard, histopathological examination. METHODS: Eleven human temporal bones were implanted with the 'Straight Research Array' (SRA). Post-insertion, they were imaged with a prototype mobile C-arm for intraoperative CBCT. Post-acquisition processing of low-dose CBCT images produced high-resolution 3D volumes with sub-millimetre spatial resolution (isotropic 0.2 mm(3) voxels). The bones were resin impregnated and sectioned for light microscopic examination. Dimensional electrode characteristics visible in section images were compared with corresponding CBCT images by independent observers. RESULTS: Overall, CBCT demonstrated adequate resolution to detect: 1) scala implanted; 2) kinking; 3) number of intracochlear contacts; 4) appropriate ascension of the array; and overall confirms ideal insertion. CBCT did not demonstrate adequate resolution to detect reversal of electrode contacts or basilar membrane rupture.
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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 | 0.005 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".