Cataract in leprosy patients: cataract surgical coverage, barriers to acceptance of surgery, and outcome of surgery in a population based survey in Korea
Bibliographic record
Abstract
BACKGROUND/AIMS: Cataract is the leading cause of blindness in leprosy patients. There is no population based information on the cataract surgical coverage, barriers to use of surgical services, and outcome of surgery in these patients. We sought to determine these measures of cataract programme effectiveness in a cured leprosy population in South Korea. METHODS: The population consisted of residents of six leprosy resettlement villages in central South Korea. All residents were invited to participate in a study of eye disease and interviewed regarding use of surgical services and reasons for not using these services. RESULTS: The cataract surgical coverage in this population was 55.4% when <6/18 was used as the cut off and increased to 78.3% when the cut off was <6/60. Barriers reported by patients included being told by the doctor that the cataract was not mature and a perception by the patient that there was no need for surgery. Among patients who had aphakic surgery, 71% were still blind in the operative eye while among patients who had pseudophakic surgery, 14% were still blind (presenting vision). Blindness in pseudophakic patients could be reduced to 3% with spectacle correction. CONCLUSION: Cataract prevalence in leprosy patients will increase as life expectancy continues to increase. Leprosy control programmes will need to develop activities aimed at reducing the burden of cataract. Recommendations include establishing collaborative agreements with ophthalmological services to provide high quality IOL surgery to these patients, training of health staff to identify and refer patients in need of surgery, monitoring the uptake of cataract surgery among patients needing services, and monitoring the outcome of surgery to improve refractive outcome.
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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 | 0.001 | 0.001 |
| 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".