Clinical Evaluation of Dynamic Visual Acuity in Subjects With Unilateral Vestibular Hypofunction
Bibliographic record
Abstract
OBJECTIVES: The objectives of this study are threefold: 1) to examine the effect of frequency of head motion on the clinical dynamic visual acuity (DVA) score in subjects with unilateral vestibular hypofunction (UVH); 2) to compare DVA scores between subjects with UVH and subjects with a complete unilateral vestibular deficit; and 3) to establish whether a relationship exists between the extent of the vestibular deficit and the DVA score. DESIGN: Experimental study. SETTING: Vestibular outpatient rehabilitation program. METHODS: A convenience sample of 10 subjects with UVH. MAIN OUTCOME MEASURES: Dynamic visual acuity scores were recorded using 2 standard acuity charts: Snellen and E-chart. The DVA scores were obtained at slow (0.5 Hz), moderate (1 and 1.5 Hz), and fast (2.0 Hz) frequencies of head motion in the horizontal and the vertical planes. Percentage of caloric weakness was compared with DVA scores in each subject to test whether a relationship exists between the two. RESULTS: As the frequency of head motion increased, the number of UVH subjects with an abnormal DVA score increased. Subjects with an abnormal DVA score at 1 Hz had the same or higher score as the frequency of the head motion was increased. Spearman correlation analyses revealed low-correlation coefficients between percentage of vestibular paresis at the caloric test and DVA scores (horizontal direction: r = 0.31, p = 0.38 for Snellen chart and r = -0.33, p = 0.35 for the E-chart; vertical: r = 0.05, p = 0.91 for the Snellen chart and r = -0.28, p = 0.50 for the E-chart). CONCLUSION: Subjects with UVH manifest impaired DVA. The frequency of head motion has an impact on clinical DVA scores in UVH subjects.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".