Axial Length and Associated Factors in Children: The Shandong Children Eye Study
Bibliographic record
Abstract
PURPOSE: To investigate ocular axial length (AL) in Chinese children. METHODS: The Shandong Children Eye Study included 6,364 children aged 4-18 years. RESULTS: Longer AL (mean 23.45 ± 1.20 mm, range 18.80-28.59 mm) was associated (multivariate analysis; correlation coefficient r2: 0.61) with older age (p < 0.001, standardized correlation coefficient β = 0.35, unstandardized regression coefficient B = 0.13, 95% confidence interval [CI] 0.12, 0.15), male gender (p < 0.001, β = -0.24, B = -0.10, 95% CI -0.29, -0.19), urban region (p < 0.001, β = 0.10, B = 0.25, 95% CI 0.20, 0.31), body height (p < 0.001, β = 0.22, B = 0.02, 95% CI 0.01, 0.02), maternal education (p < 0.001, β = 0.07, B = 0.07, 95% CI 0.05, 0.10), paternal myopia (p < 0.001, β = 0.09, B = 0.26, 95% CI 0.20, 0.33), maternal myopia (p < 0.001, β = 0.08, B = 0.23, 95% CI 0.17, 0.30), more time spent indoors reading/writing (p < 0.001, β = 0.05, B = 0.03, 95% CI 0.02, 0.04), less time spent outdoors (p = 0.005, β = -0.03, B = -0.01, 95% CI -0.02, -0.003), longer corneal curvature radius (p < 0.001, β = 0.36, B = 1.63, 95% CI 1.53, 1.74) and higher intraocular pressure (p = 0.008, β = 0.03, B = 0.01, 95% CI 0.004, 0.02). High axial myopia (AL ≥26.0 mm) present in 202 children (3.4 ± 0.2%, 95% CI 2.92, 3.84) was associated with less time spent outdoors (p = 0.002, odds ratio 0.92, 95% CI 0.87, 0.97) in multivariate analysis. CONCLUSIONS: In children in the less developed Eastern Chinese province of Shandong, the prevalence of high axial myopia was >10% among 16-year-olds. A modifiable factor associated with higher prevalence of high axial myopia was less time spent outdoors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".