The Teller Acuity Cards Are Effective in Detecting Amblyopia
Bibliographic record
Abstract
PURPOSE: Detection of amblyopia in infants and toddlers is difficult because the current clinical standard for this age group, fixation preference, is inaccurate. Although grating acuity represents an alternative, studies of preschoolers and schoolchildren report that it is not equivalent to the gold standard optotype acuity. Here, we examine whether the Teller Acuity Cards (TAC) can detect amblyopia effectively by testing children old enough (7.8 +/- 3.6 years) to complete optotype acuity testing. METHODS: Grating acuity was assessed monocularly in 45 patients with unilateral amblyopia, 44 patients at risk for amblyopia, and 37 children with no known vision disorders. Each child's grating acuity was classified as normal/abnormal based on age-appropriate norms. These classifications were compared with formal amblyopia diagnoses. RESULTS: Grating acuity was finer than optotype acuity among amblyopic eyes (medians: 0.28 vs. 0.40 logMAR, respectively, p < 0.0001) but not among fellow eyes (medians: 0.03 vs. 0.10 logMAR, respectively, p = 0.36). The optotype acuity-grating acuity discrepancy among amblyopic eyes was larger for cases of severe amblyopia than for moderate amblyopia (means: 0.64 vs. 0.18 logMAR, respectively, p = 0.0001). Nevertheless, most cases of amblyopia were detected successfully by the TAC, yielding a sensitivity of 80%. Furthermore, grating acuity was relatively sensitive to all amblyopia subtypes (69 to 89%) and levels of severity (79 to 83%). CONCLUSIONS: Although grating acuity is finer than optotype acuity in amblyopic eyes, most children with amblyopia were identified correctly suggesting that grating acuity is an effective clinical alternative for detecting amblyopia.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".