Glaucoma-Related Differences in Gaze Behavior When Negotiating Obstacles
Bibliographic record
Abstract
PURPOSE: Safe navigation requires avoiding objects. Visual field loss may affect how one visually samples the environment, and may thus contribute to bumping into objects and falls. We tested the hypothesis that gaze strategies and the number of collisions differ between people with glaucoma and normally sighted controls when navigating around obstacles, particularly under multitasking situations. METHODS: Twenty persons with moderate-severe glaucoma and 20 normally sighted controls walked around a series of irregularly spaced vertical obstacles under the following three conditions: walking with obstacles only, walking and counting backward to simulate a conversation, and walking while performing a concurrent visual search task to simulate locating a landmark. We quantified gaze patterns and the number of obstacle contacts. RESULTS: < 0.05). CONCLUSIONS: Glaucoma alters gaze patterns when negotiating a series of obstacles and increases the likelihood of collisions. Multitasking in this situation exacerbates these changes. TRANSLATIONAL RELEVANCE: Understanding glaucoma-related changes in gaze behavior during walking in cluttered environments may provide critical insight for orientation and mobility specialists and guide the design of gaze training interventions to improve mobility.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".