A 3D benign paroxysmal positional vertigo model for study of otolith disease
Bibliographic record
Abstract
OBJECTIVE: To develop a three-dimensional study tool of the membranous labyrinth in order to study the pathophysiology, diagnostic workup and treatment of benign paroxysmal positional vertigo (BPPV). BPPV is the most common cause of peripheral vertigo. Its diagnosis and treatment depend on an understanding of the anatomy of the vestibular labyrinth and its position relative to the head. To date, many illustrations have been made to explain principals of diagnosis and treatment of BPPV, but few have been based on anatomical studies of the membranous labyrinth. METHODS: A cadaveric human membranous labyrinth was axially sectioned at 20 μm resolution, stained and segmented to create a high-resolution digital model. The model was cloned to create an enantiomeric pair of labyrinths. These were associated a 3D model of a human skull, segmented from MRI data, and were oriented according to established anatomic norms. Canal markers representing otoliths were created to mark canalith position during movement of the model within the 3D environment. RESULTS: The model allows visualization of true membranous labyrinth anatomy in both ears simultaneously. The dependent portion of each semicircular duct and of the utricle can easily be visualized in any head position. Moveable markers can mark the expected progress of otolith debris with changes in head position and images can be captured to document simulations. The model can be used to simulate pathology as well as diagnostic maneuvers and treatment procedures used for BPPV. The model has great potential as a teaching tool. CONCLUSION: A simple model based on human anatomy has been created to allow careful study of BPPV pathophysiology and treatment. Going forward, this tool could offer insights that may lead to more accurate diagnosis and treatment of BPPV.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".