Binocular Vision and Eye Movement Disorders in Older Adults
Bibliographic record
Abstract
PURPOSE: To determine the prevalence of binocular vision (BV) and eye movement disorders in a clinic population of older adults. METHODS: Retrospective clinic data were abstracted from files of 500 older patients seen at the University of Waterloo Optometry Clinic over a 1-year period. Stratified sampling gave equal numbers of patients in the 60 to 69, 70 to 79, and 80+ age groups. Data included age, general and ocular history and symptoms, use of antidepressants, a habit of smoking, refraction, visual acuity, BV and eye movement status for the most recent full oculo-visual assessment, and an assessment 10 years prior. The prevalence of any BV or eye movement abnormal test (AT) result, defined as a test result outside the normal range, was determined. This included strabismus (any) or phoria; incomitancy; poor pursuits; and remote near point of convergence (NPC). The prevalence of significant BV disorders (diagnostic entities, i.e., a clinical condition that may need treatment and may have functional implications) was also determined. RESULTS: The prevalence of any BV or eye movement at was 41%, 44%, and 51% in the 60 to 69, 70 to 79, and 80+ age groups, respectively. These figures were lower for 10 years earlier: 31%, 36%, and 40% for ages 50 to 59, 60 to 69, and 70+, respectively. The prevalence of any BV or eye movement disorder was 27%, 30%, and 38% for the three age groups and 17%, 19%, and 24% for 10 years prior. Age and use of antidepressants most commonly predicted BV or eye movement AT or disorder. CONCLUSIONS: BV disorders are common among older adults.
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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 | 0.001 |
| 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".