Effectiveness of skull X-RAY to determine cochlear implant insertion depth
Bibliographic record
Abstract
BACKGROUND: Cochlear implant (CI) insertion depth can affect residual hearing preservation, tonotopic range coverage, and Mapping. Therefore, determining insertion depth has the potential to maximize CI performance. A post-op skull X-RAY is commonly used to assess insertion depth, however its effectiveness has not been well established. Our primary objective was to assess the accuracy of post-op skull X-RAYs to determine insertion depth, compared to CT as the gold standard. Secondary objectives were to compare experience level of raters and different skull X-RAY views. METHODS: Thirteen patients with Advanced Bionic HiRes 90 K implants, and post-operative temporal bone CT scans were selected from the CI database at Sunnybrook Health Sciences Centre. Medical students, otology fellows, and CI surgeons evaluated insertion depths on post-op skull X-RAYs, while neuroradiologists evaluated CT scans. Descriptive statistics, regression analysis, and paired t-tests were used to compare the two types of imaging. RESULTS: X-RAYs and CTs provided an equivalent mean insertion depth of 337 degrees (p = 0.93), a mean difference of - 0.9 degrees and a standard deviation of paired differences of 43 degrees. Although means were similar across rater groups, CI surgeons (45 degrees) had the lowest standard deviation of paired differences. Comparing X-RAY views, Caldwell (29 degrees) had less variation than Towne (59 degrees) for standard deviation of paired differences. CONCLUSIONS: Skull X-RAYs provide accurate and reliable measurements for CI insertion depth. The Caldwell view alone may be sufficient for evaluations of insertion depth, and experience has a minor impact on the variability of estimates.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".