Defining the Nature of Motion Perception Deficits in Glaucoma Using Simple and Complex Motion Stimuli
Bibliographic record
Abstract
PURPOSE: The purpose of this study is to determine the nature of motion perception deficits in primary open-angle glaucoma by measuring the sensitivity of simple (luminance-defined) and complex (texture-defined) motion, the latter requiring supplementary neural processing to be resolved. These findings will help address the possible extent of the cortical damage in glaucoma that has been recently demonstrated by anatomic and physiological studies. They also serve the purpose of establishing which motion paradigms would be most appropriate for assessing glaucoma-related functional loss. METHODS: Direction-identification thresholds for first-order and second-order motion were measured for 26 patients with primary open-angle glaucoma (for both phakic and pseudophakic) and 18 nonglaucomatous observers. RESULTS: The glaucomatous observers showed significantly increased motion thresholds for both first- and second-order motion conditions when compared with nonglaucomatous observers. However, the relative increase in threshold for first-order motion did not differ significantly from that of second-order motion. CONCLUSIONS: These findings imply that there is no measurable higher-level cortical function damage caused by the glaucomatous process because no greater loss in second-order motion was observed. Based on the results, we suggest that motion paradigms used to assess functional loss in primary open-angle glaucoma should consist of simple, first-order type stimuli to minimize potential confounds such as those introduced by both the normal and pathologic aging process on complex motion processing (i.e., perimetry using complex motion stimuli).
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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 | 0.001 |
| 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".