Blindness and eye disease in a Tibetan region of China: findings from a Rapid Assessment of Avoidable Blindness survey
Bibliographic record
Abstract
INTRODUCTION: The only population-based survey of blindness and visual impairment of a Tibetan population was conducted in the Tibet Autonomous Region in 1999. METHODS AND ANALYSIS: The Rapid Assessment of Avoidable Blindness methodology was used to conduct a survey of Kandze Tibetan Autonomous Prefecture, Sichuan Province of China in the Fall 2017. Using the 2010 census, 100 clusters of 50 participants aged 50 years or older were randomly sampled using probability proportionate to size. RESULTS: Among the 5000 people enumerated, 4763 were examined (95.3% response). The age-adjusted and sex-adjusted prevalence of blindness, severe visual impairment, moderate visual impairment and early visual impairment (EVI) were 1.6% (95% CI: 1.08 to 2.38)), 0.9% (95% CI:0.7 to 1.5), 5.1% (95% CI:4.4 to 5.7), and 7.45% (95% CI:6.67 to 8.2), respectively. The prevalence of blindness among Tibetans was significantly higher than that among Han Chinese (2.2% (95% CI:1.8 to 2.6) and 0.6 (95% CI:0.2 to 1.7), respectively, p<0.05). Women bore a significant excess burden of EVI compared with men (8.5% (95% CI:7.5 to 9.6) and 6.1% (95% CI:5.1 to 7.2), respectively, p<0.05). Cataract was the primary cause of blindness (39.4%) followed by macular degeneration (10.6%) and corneal opacity (5.3%). CONCLUSION: Blindness and visual impairment in Kandze Tibetan Autonomous Prefecture is substantially less than an earlier study of a Tibetan region and now resembles other regions of China. About 58% of blindness and 67% of SVIwere avoidable, primarily by providing cataract services. Eighty-three percent of EVI was avoidable by providing refractice services throughout the region.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.001 | 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".