The Prevalence of Lens Opacities in Tehran: The Tehran Eye Study
Bibliographic record
Abstract
PURPOSE: To determine the prevalence of lens opacities and cataract surgical coverage among Tehran citizens 40 years of age and older. METHODS: The Tehran Eye Study was a population-based survey, with random sampling from Tehran household clusters. Those 40 years of age and older from that survey were included in this analysis. All participants underwent full optometric, slit lamp, and fundoscopic examinations. Lens opacity was assessed after pupil dilation using the modified Lens Opacities Classification System III (LOCS III). The main indices for prevalence of cataract were any lens changes, defined as the presence of a gradable cataract in one or both eyes, and all lens changes, defined as any lens changes plus a history of cataract surgery. RESULTS: A total of 1434 participants were included in this analysis; 305 of which met the criteria for all lens changes resulting in an adjusted prevalence of 22.7% (CI95%: 20.2%-25.3%). The prevalence was 21.2% among men and 24.5% among women. The prevalence of any lens changes was 19.1% (CI95%: 16.6%-21.6%) and the prevalence was higher in women. The prevalence for both indices increased with age. Considering their better eyes, 39 people (2.7%) were shown to have low vision because of cataract and another 12 (0.8%) were classified as blind. CONCLUSIONS: Cataract has affected approximately one-fifth of the Tehran population aged 40 years and over, women more than men, and has severely affected the vision of approximately 3.5% of this population. We found that access to cataract surgery facilities was not an issue.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".