Validation of the Waterloo Differential Visual Acuity Test (WatDAT) and Comparison With Existing Pediatric Tests of Visual Acuity
Bibliographic record
Abstract
Purpose: The new Waterloo Differential Acuity Test (WatDAT) is designed to allow recognition visual acuity (VA) measurement in children before they can typically undertake matching tests. The study purpose was to validate WatDAT in adults with normal and reduced VA. Methods: Eighty adults (18 to <40 years of age) participated (32 normal VA, 12 reduced VA, and 36 simulated reduced VA). Monocular VA was measured on two occasions in random order for WatDAT (versions with 3 and 5 distractors for Faces and Patti Pics house among circles), Lea Symbols, Kay Pictures and Patti Pics matching tests, Teller Acuity Cards, Cardiff Acuity Test, and Early Treatment Diabetic Retinopathy Study (ETDRS) letter chart. Pediatric tests were validated against ETDRS using limits of agreement (LoA), sensitivity, and specificity. The LoA for repeatability were also determined. Results: WatDAT showed minimal bias compared with ETDRS, and LoAs, which were similar to pediatric matching tests (0.241-0.250). Both preferential looking tests showed higher bias and LoAs than ETDRS. Matching tests showed good agreement with ETDRS, except for Kay Pictures and Lea Uncrowded test, which overestimated VA. WatDAT showed high sensitivity (>0.96) and specificity (>0.79), which improved with criterion adjustment and were significantly higher than for the preferential looking tests. LoA for repeatability for WatDAT 3 Faces and WatDAT 5 Faces were comparable with the ETDRS. Conclusions: WatDAT demonstrates good agreement and repeatability compared with the gold-standard ETDRS letter chart, and performed better than preferential looking tests, the alternative until a child can undertake a matching VA test. Translational Relevance: Good validity of the Waterloo Differential Acuity Test was demonstrated in adults as a first step to showing its potential for detecting childhood visual disorders.
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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 | 0.003 | 0.008 |
| 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) | 0.001 | 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".