Cost-effectiveness of the anti-vascular endothelial growth factor intravitreal injection and panretinal photocoagulation for patients with proliferative diabetic retinopathy in South Korea
Bibliographic record
Abstract
Abstract Background: We determined the cost-effectiveness of the anti-vascular endothelial growth factor (VEGF) intravitreal injection versus panretinal photocoagulation (PRP) for patients with proliferative diabetic retinopathy (PDR) in South Korea. Methods: We simulated four treatment strategies using PRP and the anti-VEGF injection by constructing a Markov model for a hypothetical cohort of 50-year-old PDR patients: (1) PRP only; (2) anti-VEGF injection only; (3) PRP first; and (4) anti-VEGF injection first. Results: In this cost-effectiveness analysis, compared with only-PRP, the incremental cost-effectiveness ratiowas $95,456 per quality-adjusted life-year (QALY) for PRP first, $34,375 per QALY for anti-VEGF injection first, and $33,405 per QALY for anti-VEGF injection only from a healthcare perspective. From the societal and payer perspective, strategy (2) was more cost-saving and effective than (1). In the probabilistic sensitivity analysis, only-PRP was cost-effective up to the willingness-to-pay (WTP) of about $42,000, while anti-VEGF injection only was cost-effective from a healthcare perspective. From the societal and payer perspectives, regardless of the value of WTP, anti-VEGF injection only was the most cost-effective strategy. Conclusion: Our results on the cost-effectiveness of the anti-VEGF injection for PDR, alone or in combination with PRP treatment, can be used as important evidence when making medical service decisions.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".