Microeconomic Benefit of Corneal Transplantation in a Developing Country via Public–Private Partnership Model
Bibliographic record
Abstract
BACKGROUND: We measured the microeconomic benefit, QOL, DALYs averted and clinical outcomes of corneal transplant surgery via a public-private partnership in Guyana. Corneas were obtained, ex gratia, from US eye banks, and the work was done at no cost to the patient or the Governments of USA or Guyana. METHODS: We obtained qualitative data using a "semi-structured interview technique" to question 60 recent recipients of corneal transplants in Guyana. Our questions covered schooling in children, training for job, and type and income of job, both before and after surgery. We also discussed improvement in family income and quality of life (QOL) using a Likert scale of 1 lowest to 5 highest. RESULTS: Our data came from five humanitarian missions from July 2014 to July 2017. All school-going children (n = 6) were able to return to school and participate in educational activities. Young adults (n = 13), were able to acquire new jobs (50%) or training positions (50%) with higher income. Patients in the middle-age adult group (n = 20) re-acquired their employment positions (25%) or found new work (75%). Elderly patients (n = 21) after transplant were able to perform odd jobs to increase the family income. A consistent theme across all age groups was a dramatic improvement in the QOL. Two hundred and sixty DALYs (4.3 per patient) were averted. In this cohort of 62 surgery cases, mean preoperative visual acuity was 0.03 and postoperative mean visual acuity was 0.20. CONCLUSIONS: We have shown microeconomic benefits and improved QOL of corneal transplantation in a low-income country.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".