Validity, Reliability, and Repeatability of the Useful Field of View Test in Persons with Normal Vision and Patients with Glaucoma
Bibliographic record
Abstract
PURPOSE: To determine the validity, test-retest reliability and repeatability of the UFOV test in healthy controls and glaucoma patients. METHODS: Three substudies with the UFOV test were conducted: (1) validity was evaluated in 77 older controls (mean age 64 [SD, 7] years) and 53 glaucoma patients (mean age 69 [SD, 8] years); (2) test-retest reliability was evaluated in 13 young controls (mean age 28 [SD, 4] years), 21 older controls (mean age 66 [SD, 9] years), and 22 glaucoma patients (mean age 68 [SD, 8] years) who performed the test twice within approximately two weeks; (3) repeatability was evaluated in 17 young controls (mean age 33 [SD, 8] years) who performed the test five times on the same day. RESULTS: In the validity substudy, mean total processing time was significantly less for older controls (358.3 ms [SD, 226.8 ms]), than glaucoma patients (580.2 ms [SD, 324.5 ms]), with moderate correlations (rho ≥ 0.40) between total processing time and age, and visual field impairment. In the reliability substudy, mean total processing time was significantly less on retest (P ≤ 0.02), with glaucoma patients showing the largest mean test-retest difference (144.7 ms [SD, 168.9 ms]) compared with young (31.5 ms [SD, 43.7 ms]) and older controls (56.2 ms [SD, 74.8 ms]). The 95% limits of agreement were significantly wider for glaucoma patients (-186.3 and +475.7 ms) compared with young (-54.1 and +117.1 ms) and older controls (-90.5 and +202.9 ms), (P < 0.01). In the repeatability substudy, performance remained constant after the second of five tests (differences in mean total processing time <6 ms). CONCLUSIONS: Measurement properties of the UFOV test are important for assessing functional performance, in particular, fitness to drive. Our results indicate moderate variability, greater for glaucoma patients than healthy controls, and a learning effect. Two consecutive tests are suggested to establish reliable baseline measures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.009 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".