Synthesizing evidence about developmental patterns in human visual acuity as measured by Teller Acuity Cards
Bibliographic record
Abstract
Replication is a cornerstone of scientific rigor and a prerequisite for cumulative science. This project synthesized evidence from published research that employed a widely used measure of grating visual acuity (VA), Teller Acuity Cards (TAC). We sought to capture findings about the development of VA in early childhood into an aggregated dataset and share the dataset openly. Online literature searches identified papers that mentioned “teller acuity cards”, “visual acuity cards”, or “teller cards”. We found n=745 papers published from 1974-2024. Next, we identified empirical papers that used TAC to measure VA and which reported VA in an extractable tabular form. To-date, n=250 of 316 papers with available PDF versions have been evaluated and n=14 have been identified that present extractable data meeting our screening criteria. Available datasets represent more than n=3,991 participants and 7 countries (Australia, Brazil, Canada, China, Italy, Mexico, and the U.S.). As expected, group VA increases from birth to 36-months, with faster rates of change among children tested binocularly (0.47 cyc/deg per month) than those tested monocularly (0.35 cyc/deg per month). Group VA values at similar ages vary substantially across studies, especially in children older than 12 months. Our synthesis of published TAC VA data confirms anticipated age-related trends and points to avenues for future research, particularly regarding what factors account for cross-study and by-country differences in rates of development. We hope our soon-to-be openly shared dataset contributes toward a more cumulative science of visual development.
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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 | 0.060 | 0.356 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.034 | 0.022 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".