Mobile phone app Vs bucket test as a subjective visual vertical test: A validation study
Bibliographic record
Abstract
BACKGROUND: The SVV tests the ability of a person to perceive the gravitational vertical. A tilt in SVV indicates vestibular imbalance in the roll plane, and thus injuries to the utricle or its connecting nerves. A validated bedside method (et, al., 2009, 72(19):1689-1692, Neurol, Zwergal) is the bucket method, in which the subject estimates the true vertical by attempting to properly align a straight line visible on the bottom of a bucket that is rotated at random by the examiner. In our study, the subjects need to align the plumb line on the Visual Vertical iOS app to the vertical direction. METHODS: Measurements of the SVV were made in 22 healthy subjects (16 females and 6 males). Each subject conducted 10 iterations of bucket test and 10 iterations of iOS app test. The reliability and validity of the iOS app was analyzed by SPSS21. RESULTS: Cronbach's α for the plumb line method was 0.976, and the iOS app was 0.978. Statistical comparison of SVV values measured by the iOS app and the bucket method showed no significant difference in distribution (Mann Whitney U test U = 0.944). CONCLUSION: The Visual Vertical iOS app is an effective and accessible substitute to the plumb line for the measurement of the validated bucket test.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".