Subjective Visual Vertical and Subjective Visual Horizontal Measures in Patients with Chronic Dizziness
Bibliographic record
Abstract
OBJECTIVES: We aimed to find the frequency of otolith organ pathologies in the clinical picture of common dizziness etiologies in the chronic stage. METHOD: Subjective visual vertical and subjective visual horizontal measures were assessed in patients who had persistent or recurrent dizziness at least 2 months after the acute period. Every patient was tested in three head positions: neutral, right, or left deviation in the roll plane. Test results were compared with those of the control group. RESULTS: Seventy-three patients and 18 controls were examined. Fifty-eight of the patients had peripheral vestibular disease; 15 of them had central vestibular disease. Left subjective visual horizontal (SVH) and right SVH measures of the peripheral group were significantly different from those of the control group (p < .01). There was no difference in any test between the peripheral and central groups. When we put a cut off point for abnormality (0, 1) according to mean +/- 2 SD of the control group, the peripheral and central groups had very high significant differences from the control group. Approximately 25 to 50% of our patients had pathologic subjective visual vertical or SVH measures according to test type. CONCLUSION: These results showed that the otolith system must be evaluated in the chronic period of dizziness, especially in patients who frequently visit their physician, and modifications in treatment programs must be conducted.
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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 | 0.003 |
| 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".