Caloric and Rotational Testing: Merits, Pitfalls and Myths
Bibliographic record
Abstract
Objectives: The evaluation of the caloric and the rotational tests in the light of contemporary data on the function and dysfunction of the vestibular system. This is attempted by using novel engineering system notions. Material and Patients: Our work in mathematical modeling of the vestibuloocular reflex. In addition, our data from thousands of patients submitted to caloric testing and more than one hundred patients submitted to rotational testing. Methods: Analysis of results in light of system engineering notions. Results and Conclusions: The caloric test, corresponding to low head velocity and movement frequency, is an excellent test in defining the degree of excitability of one side versus the other. If there is a bilateral loss of vestibular function both, right and left, caloric responses will be compromised. However, the definition of the dynamic condition of the system and the detection of nonlinearities, especially in the presence of pathology, require rotational tests, which will yield such information if data are appropriately processed. Present day knowledge is insufficient on: (1) the functional contribution of the fast phase of nystagmus, (2) the synergistic effects of semicircular canals and otoliths, and (3) the synergistic effects of vestibular and other sensory modalities.
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 | 0.047 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.029 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.010 |
| 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".