Intra- and inter-hemispheric processing during binocular rivalry in mild glaucoma
Bibliographic record
Abstract
Glaucoma is considered a progressive optic neuropathy because of the damage and death of the retinal ganglion cells. It is also a neurodegenerative disease because it affects neural structures in the visual system and beyond, including the corpus callosum-the largest white matter structure involved in inter-hemispheric transfer of information. In this study we probed the dysfunction of the inter-hemispheric processing in patients with mild glaucoma using the phenomenon of binocular rivalry. Patients with mild glaucoma and no measurable visual field defects and age-matched controls underwent a thorough visual assessment. Then they participated in a series of psychophysical tests designed to examine the binocular rivalry derived from intra- and inter-hemispheric processing. Static horizontal and vertical sinewave gratings were presented dichoptically using a double-mirror stereoscope in 3 locations: centrally, to probe inter-hemispheric processing, and peripherally to the left or to the right, to probe intra-hemispheric processing. Although the two groups were matched in functional measures, rivalry rate of the glaucoma group was significantly lower than that of the control group for the central location, but not for the peripheral location. These results were driven mainly by the patients with normal tension glaucoma whose average rivalry rate for the central location (from which information reaches the two hemispheres) was almost half (46% lower) that of the controls. These results indicate a dysfunction in inter-hemispheric transfer in mild glaucoma that can be detected behaviourally before any changes in standard functional measures.
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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 | 0.001 |
| 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) | 0.001 | 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".