The Moving Dynamic Random Dot Stereosize Test: Development, Age Norms, and Comparison With the Frisby, Randot, and Stereo Smile Tests
Bibliographic record
Abstract
PURPOSE: To determine the response of infants and children to the Moving Dynamic Random Dot Stereosize (MDRS) test and to collect cross-sectional age-related data. METHODS: Sixty visually normal individuals were divided into four age groups: 0.5-<2, 2-<5, 5-<8, and 8-<20 years. Stereopsis was measured with the MDRS test on two occasions, plus the Frisby, Randot, or Stereo Smile tests, as was age appropriate. RESULTS: All children aged >2 years and 80% of the children between ages 6 months and 2 years were able to perform the MDRS test on at least one occasion. Sixty percent of the 6-month to 2-year-old children were able to perform the Stereo Smile test on both occasions. Performance on the MDRS test improved with age up to 9 years. Improvement on the Frisby and Randot tests was seen in children aged up to 7 years. Mean and 95% confidence interval ranges for each test are given. CONCLUSION: This study gives evidence that aspects of the visual system are not fully mature until age 7-9 years. The MDRS test is a visually demanding but cognitively simple test that shows potential for detecting visual anomalies in young children.
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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 | 0.007 |
| 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) | 0.001 | 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".