Clinical performance analysis and cost-utility analysis of a mobile cataract surgery service in a rural setting in Thailand
Bibliographic record
Abstract
• Mobile cataract surgery improves visual outcomes and quality of life. • A hybrid approach with Phaco and ECCE is suitable for advanced cataracts. • ECCE offers cost-effective cataract care for advanced cases in rural settings. • Phaco delivers superior visual outcomes and cost-effectiveness when scaled. • Study supports mobile services to boost cataract surgery access per WHO goals. To evaluate a clinical performance and cost-effectiveness of a mobile cataract surgery service, specifically and separately for Phacoemulsification (Phaco) and extracapsular cataract extraction (ECCE) A pre-post study with individual-level data Patients diagnosed with advanced cataracts underwent cataract surgery at a mobile cataract surgery service. Data on corrected distance visual acuity (CDVA), quality of life (QOL) assessed by EQ-5D-5L questionnaire, and cost were prospectively collected at and compared between baseline and 3 months after treatment. Linear regression was used to analyze the clinical performance, and a cost-utility analysis (CUA) was conducted to show the cost-effectiveness of the mobile service. A total of 98 eyes from 98 patients had cataract surgeries, with 53 patients (54 %) undergoing Phaco techniques and 45 patients (46 %) undergoing ECCE techniques. For the total cohort, LogMAR CDVA changed from 1.88±0.59 to 0.23±0.23 ( p < 0.001) with only one case of complication. Significant improvements of mobility and usual activities QOL scores, and utility values were demonstrated (all p < 0.001). Separate CUAs by technique with pre-post data showed that patients with Phaco gained 4.93 QALYs and costed approximately THB 18,800 (USD 535) and patients with ECCE gained 5.45 QALYs with the cost of THB 14,400 (USD 412). A mobile cataract surgery service is effective in improving vision and QOL with a low complication rate. The CUAs showed that both Phaco and ECCE could be cost-effective. Implementing such services in rural areas could be a strategy to increase effective cataract surgery coverage.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".