The cost-effectiveness of early vitrectomy for the treatment of vitreous hemorrhage in diabetic retinopathy
Bibliographic record
Abstract
Diabetic vitrectomy has been found to be efficacious for the treatment of vitreous hemorrhage secondary to diabetic retinopathy. The purpose of this study is to determine the cost-effectiveness of early vitrectomy for the management of vitreous hemorrhage secondary to diabetic retinopathy. The analysis was performed from the perspective of a third-party insurer. A cost-utility Markov model was used to determine the cost per quality-adjusted life year (QALY) gained from early versus deferral of vitrectomy. The model used 2-, 3-, and 4-year results from the Diabetic Retinopathy Vitrectomy Study, patient-based utilities, life expectancy data, and incremental medical costs. Early vitrectomy was the dominant strategy and was associated with a gain of 0.41 QALYs over the 57-year expected life span for a hypothetical patient. The cost per additional QALY gained from early vitrectomy treatment was $1910 (US$ discounted at 3%). When sensitivity analyses were performed by varying efficacy probabilities and utilities across their 95% confidence intervals, early treatment was always the dominant strategy. Additionally, even at the extreme sensitivity values, the cost per QALY of early vitrectomy treatment remained under $10,000. Overall, early vitrectomy for the treatment of vitreous hemorrhage secondary to diabetic retinopathy is highly cost-effective.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".