Exploring the effect of glaucomatous visual field defects of current drivers on a neuropsychological test battery
Bibliographic record
Abstract
PURPOSE: This study explores the effect of glaucomatous visual field defects on several neuropsychological tests that are often used in research and in clinical settings. METHODS: Nineteen glaucoma patients and nineteen healthy participants, which are current drivers and older than 65 years old were included. All participants completed the Montreal Cognitive Assessment (MoCA), the Trail Making Test (TMT), the Benton Visual Retention Test (BVRT), the Snellgrove Maze Task (SMT) and the Digit Span Test (DST). All participants were also tested on contrast sensitivity and near and far visual acuity. For the glaucoma patients, visual field tests were downloaded from hospital servers. RESULTS: On the MoCA test, glaucoma patients scored lower than the healthy group, but not significantly. On the MoCA-Blind, the difference was statistically significant. Glaucoma patients also had lower percentile scores on the TMT, with a significant difference in the TMT-A, but this difference largely disappeared in the calculated TMT B-A index, which isolates the cognitive component. The BVRT and SMT showed no significant differences between both groups. In the only non-visual test, the DST, glaucoma patients outperformed the healthy group. Glaucoma severity did not influence results, except for the BVRT on which the moderate/severe group has better scores. CONCLUSION: Using visual items might lead to conclusions about cognition when it should be one about vision. Therefore, careful selection of tests is needed when examining cognition in glaucoma patients.
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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 | 0.001 | 0.004 |
| 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".