Cataract surgical coverage and outcome in the Tibet Autonomous Region of China
Bibliographic record
Abstract
BACKGROUND: A recently published, population based survey of the Tibet Autonomous Region (TAR) of China reported on low vision, blindness, and blinding conditions. This paper presents detailed findings from that survey regarding cataract, including prevalence, cataract surgical coverage, surgical outcome, and barriers to use of services. METHODS: The Tibet Eye Care Assessment (TECA) was a prevalence survey of people from randomly selected households from three of the seven provinces of the TAR (Lhoka, Nakchu, and Lingzhr), representing its three main environmental regions. The survey, conducted in 1999 and 2000, assessed visual acuity, cause of vision loss, and eye care services. RESULTS: Among the 15,900 people enumerated, 12,644 were examined (79.6%). Cataract prevalence was 5.2% and 13.8%, for the total population, and those over age 50, respectively. Cataract surgical coverage (vision <6/60) for people age 50 and older (85-90% of cataract blind) was 56% overall, 70% for men and 47% for women. The most common barriers to use of cataract surgical services were distance and cost. In the 216 eyes with cataract surgery, 60% were aphakic and 40% were pseudophakic. Pseudophakic surgery left 19% of eyes blind (<6/60) and an additional 20% of eyes with poor vision (6/24-6/60). Aphakic surgery left 24% of eyes blind and an additional 21% of eyes with poor vision. Even though more women remained blind than men, 28% versus 18% respectively, the different was not statistically significant (p = 0.25). CONCLUSIONS: Cataract surgical coverage was remarkably high despite the difficulty of providing services to such an isolated and sparse population. Cataract surgical outcome was poor for both aphakic and pseudophakic surgery. Two main priorities are improving cataract surgical quality and cataract surgical coverage, particularly for women.
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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 | 0.000 |
| 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 | 0.000 |
| 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".