Comparison of binocular reading speed in patients with strabismus without amblyopia versus controls
Bibliographic record
Abstract
OBJECTIVE: Amblyopia has been shown to slow reading speed. Limited literature exists on reading speed in strabismus without amblyopia. Our study compares binocular reading speed in patients with strabismus without amblyopia versus normal controls. METHODS: We conducted a prospective study with 48 participants: 12 childhood-onset (onset <8 years of age) strabismus without amblyopia and 36 age- and education level-matched controls. Inclusion criteria were age 14-50 years, education >9 years, primary language English, best-corrected visual acuity >20/30 distance, and >N8 near either eye. Exclusion criteria were presence of other eye pathology or neurologic/cognitive conditions that may affect reading and previous treatment for strabismus/amblyopia. International Reading Speed Texts were used for binocular reading speed assessment. Each participant read 2 passages (passage 1 and 8), following all International Reading Speed Texts instructions. Reading time was measured using a stopwatch. Reading speed was calculated in words per minute (WPM). RESULTS: Mean age for the strabismus group was 28.3 ± 11.1 and for the control group was 28.2 ± 11.0 years (P = 0.96). Mean education level for strabismus group was 14.2 ± 2.4 and control group was 13.8 ± 2.5 years (P = 0.62). Mean binocular reading speed for passage 1 for strabismus group was 192.0 and for control group was 220.0 WPM (P = 0.01). Mean binocular reading speed for passage 8 for strabismus group was 201.3 and for control group was 226.2 WPM (P = 0.04). CONCLUSIONS: Patients with strabismus (without amblyopia) had slower binocular reading speed compared with controls. Further studies with eye tracking may provide more information. Strabismus, even without amblyopia, may affect reading performance and consequently vision-related quality of life.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.003 | 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".