The Normal Adult Human Internal Auditory Canal: A Volumetric Multidetector Computed Tomography Study
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to demonstrate that volumetric analysis of multidetector computed tomography (CT) images can be used to calculate the volume of the adult human internal auditory canal (IAC) reproducibly, and to describe the range of normal IAC volumes in the adult population with subgroup analysis of sex, age, and laterality. BACKGROUND: Previous studies of the IAC have typically used measurements in two dimensions or by using casts of cadavers to measure IAC volumes. This study is the first to report the normal ranges of IAC volumes measured by CT. METHODS: Two hundred eighty-one CT scans were assessed. Of the CT scans that met the inclusion criteria, a software package was used to manually contour the IACs in each subject to calculate the volumes in cubic millimeters. Subgroup analysis of laterality, sex, and age was evaluated. Interobserver agreement was calculated for the first 59 patients (118 canals). RESULTS: Two hundred fifty-nine scans (518 canals) met the inclusion criteria. The volumes ranged from 74 to 502 mm, with no statistically significant difference between left and right (p value = 0.69). In males, the range of volumes measured 74 to 502 mm while in females it ranged from 78 to 416 mm. Males had larger IAC volumes than females (Wilcoxon rank-sum test: S = 14,845.0, p value = 0.01 on the right, and S = 14,646, p value = 0.004 on the left). No correlation was found with age (Spearman: -0.10, p value = 0.09 on the right and -0.04, p value = 0.50 on the left). Excellent interobserver agreement was found. CONCLUSION: IAC volumes of normal adult subjects, measured by CT, were larger in males and not significantly different with respect to age or laterality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".