Software for subjective visual vertical assessment: an observational cross-sectional study
Bibliographic record
Abstract
UNLABELLED: Spatial orientation in relation to the gravitational axis is significantly important for the maintenance of the posture, gait and for most of the human's motor activities. The subjective visual vertical exam evaluates the individual's perception of vertical orientation. OBJECTIVES: The aims of this study were (1) to develop a virtual system to evaluate the subjective visual vertical exam, (2) to provide a simple tool to clinical practice and (3) to assess the subjective visual vertical values of healthy subjects using the new software. STUDY DESIGN: observational cross-sectional study. METHODS: Thirty healthy volunteers performed the subjective visual vertical exam in both static and dynamic conditions. The exam consisted in adjusting a virtual line in the vertical position using the computer mouse. For the static condition, the virtual line was projected in a white background. For the dynamic condition, black circles rotated in clockwise or counterclockwise directions. Six measurements were taken and the mean deviations in relation to the real vertical calculated. RESULTS: The mean values of subjective visual vertical measurements were: static -0.372º; ± 1.21; dynamic clockwise 1.53º ± 1.80 and dynamic counterclockwise -1.11º ± 2.46. CONCLUSION: This software showed to be practical and accurate to be used in clinical routines.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".