Clinical applications of a visual field perimeter with binocular video imaging
Bibliographic record
Abstract
Purpose The majority of visual field testing equipment is designed for monocular tests only: most instruments do not have the capability to visualize both eyes simultaneously and many do not have a head rest position suitable for binocular testing. This presentation will focus on the clinical applications of the binocular visual field tests available on the MonCvONE perimeter. Methods Binocular testing is very important for functional evaluations. Extrapolation from monocular tests is inaccurate as phenomenons such as integration, suppression, ocular deviation, cyclotorsion, … affect differently monocular and binocular vision. The MonCvONE perimeter combines a binocular video sensor with the possibility of video recording to the possibility of controlling the exam with an interactive mode similar to the Goldmann perimeter. Results Clinical examples of binocular perimetry will be presented including ‐ the evaluation of ptosis with a documentation of the gain in visual field area in relationship with the opening of the eye lids. ‐ the assessment of low vision with the Esterman technique ‐ the evaluation of drivers’ visual field in agreement with the European Community directive ‐ the evaluation of the field of single vision with the determination of a diplopia score‐ the test of the visual field in young infants when eye occlusion and head immobility are not easily obtained Conclusions These examples demonstrate the clinical usefulness of binocular visual field testing with video recording
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".