Thirty-five-year trend in the prevalence of refractive error in Austrian conscripts based on 1.5 million participants
Bibliographic record
Abstract
BACKGROUND: To quantify the current burden of myopia and hyperopia in Austrian young men and the time trend of myopia in the past 35 years by individual and social correlates. METHOD: We included data on all Austrian military conscripts from 1983 to 2017 (n=1 507 063) from six medical investigation stations. Young men provided data on education, weight and height for calculating body mass index, blood pressure and resting heart rate. Non-cycloplegic refractions were measured by an autorefractometer. Spherical equivalent was calculated by standard formula (sphere+cylinder/2, unit dioptres (D)). Myopic refractive error was defined as <-0.5 D). Hyperopic refractive error was defined as >0.5 D. RESULTS: The largest burden of refractive error in Austria is myopia, which rose from 13.8% to 24.4% over 35 years, with less than 5% hyperopic population. Over time, the prevalence of myopia was constantly lower yet increased more rapidly among those with low education levels (11.4%-21.7%) compared with those with higher education (24.5%-29.6%) in all medical investigation stations. We found consistent associations of some unfavourable health indicators (underweight: ORs 1.1-1.4, higher resting heart rate: all p trend <0.001) with higher myopia prevalence, which point towards lifestyle factors playing an important role in the development of myopia. CONCLUSION: Primary preventive measures are needed to curb the observed trend in myopia among Austrian young men. Future research should investigate the impact of modifiable factors on myopia development and progression, particularly lifestyle factors that are dramatically shifting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".