Migration study of lens opacities in Bangladeshi adults in London and Bangladesh: a pilot study
Bibliographic record
Abstract
BACKGROUND: Lens opacities (LO) occur at an earlier age and have a higher prevalence in developing countries. In this pilot study, we assessed the feasibility and practical challenges of conducting a migration study, testing the hypothesis that migration from Bangladesh to the UK decreases the amount of LO on account of less exposure to adverse environmental factors. METHODS: The sample, which was selected from East London, UK and in Bangladesh, underwent detailed examination and lens grading by the same certified grader using Lens Opacification Classification System III. Data were analysed using univariate and multivariable logistic regression analyses. RESULTS: Considerable difficulties were encountered in recruiting the sample in both locations. 372 Bangladeshis aged 40-70 years were examined: 131 in London and 241 in Bangladesh. Having never migrated from Bangladesh was an independent risk factor for opacities (OR 7.6; 95% CI 3.6 to 15.9; p=0.001) as were age (OR 7.1; 95% CI 4.0 to 12.7; p=0.001) and diabetes (OR 2.5; 95% 1.0 to 6.0; p=0.04). The odds of LO were lower among those who had lived in the UK for a higher proportion of their life (OR 0.96; 95% CI 0.93 to 0.99; p=0.01), but this was not significant after adjusting for age and diabetes (OR 0.97; 95% CI 0.94 to 1.01; p=0.16). DISCUSSION: The study highlights the challenges of migration studies, and of studies involving ethnic minorities. Preliminary findings suggest that migration to the UK is protective for LO despite a significantly higher rate of diabetes in the UK. A larger study is warranted based on these preliminary findings.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".