Vestibular animal models: contributions to understanding physiology and disease
Bibliographic record
Abstract
Our knowledge of the vestibular sensory system, its functional significance for gaze and posture stabilization, and its capability to ensure accurate spatial orientation perception and spatial navigation has greatly benefitted from experimental approaches using a variety of vertebrate species. This review summarizes the attempts to establish the roles of semicircular canal and otolith endorgans in these functions followed by an overview of the most relevant fields of vestibular research including major findings that have advanced our understanding of how this system exerts its influence on reflexive and cognitive challenges encountered during daily life. In particular, we highlight the contributions of different animal models and the advantage of using a comparative research approach. Cross-species comparisons have established that the morpho-physiological properties underlying vestibular signal processing are evolutionarily inherent, thereby disclosing general principles. Based on the documented success of this approach, we suggest that future research employing a balanced spectrum of standard animal models such as fish/frog, mouse and primate will optimize our progress in understanding vestibular processing in health and disease. Moreover, we propose that this should be further supplemented by research employing more "exotic" species that offer unique experimental access and/or have specific vestibular adaptations due to unusual locomotor capabilities or lifestyles. Taken together this strategy will expedite our understanding of the basic principles underlying vestibular computations to reveal relevant translational aspects. Accordingly, studies employing animal models are indispensible and even mandatory for the development of new treatments, medication and technical aids (implants) for patients with vestibular pathologies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".