Transimpedance Matrix Can Be Used to Estimate Electrode Positions Intraoperatively and to Monitor Their Positional Changes Postoperatively in Cochlear Implant Patients
Bibliographic record
Abstract
OBJECTIVE: Accurate positioning of the electrode array during cochlear implant (CI) surgery is crucial for achieving optimal hearing outcomes. Traditionally, postoperative radiological imaging has been used to assess electrode position. Transimpedance matrix (TIM) measurements have also emerged as a promising method for assessing electrode position. This involves utilizing electric field imaging to create an electric distance matrix by analyzing voltage variations among adjacent electrodes. This study aimed to investigate the feasibility of using intraoperative TIM measurements to estimate electrode position and monitor postoperative changes. STUDY DESIGN: Retrospective cohort study. SETTING: University Medical center, tertiary academic referral center. PATIENTS: Patients undergoing CI (CI622) surgery between January 2019 and June 2022. INTERVENTION: CI electrode positions and maximal angular insertion depths (maxAID) were determined using X-ray imaging according to Stenvers' projection. The mean gradient phase (MGP) was extracted from the TIM, and a correlation between the MGP and maxAID was examined. A model was then built to estimate the maxAID using the MGP, and changes in electrode location over time were assessed using this model. MAIN OUTCOME MEASURES: Twenty-four patients were included in this study. A positive correlation between the maxAID and the MGP ( R = 0.7, p = 0.0001) was found. The established model was able to predict the maxAID with an accuracy of 27.7 ± 4.4°. Comparing intraoperative and postoperative TIM measurements, a decrease of 24.1° ± 10.7° in maxAID over time was observed. CONCLUSION: TIM measurements are useful for estimating the insertion depth of the electrode and monitoring changes in the electrode's position over time.
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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 | 0.001 | 0.005 |
| 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 | 0.000 | 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".