A Systematic Review of Economic Evaluations Conducted for Interventions to Screen, Treat, and Manage Retinopathy of Prematurity (ROP) in the United States, United Kingdom, and Canada
Bibliographic record
Abstract
A systematic literature review (SLR) of economic evaluations (EE) conducted for interventions to screen, treat, and manage retinopathy of prematurity (ROP) in the United States (US), United Kingdom (UK), and Canada was performed. The SLR accessed the MEDLINE, Embase, Cochrane, Web of Science, Health Business Elite, Econ. Lit, NHS EED, and Google Scholar databases over the period 1<sup>st</sup> January 2000 to 4<sup>th</sup> August 2021. The key Medical Subject Heading (MeSH) search terms used included: <i>Retinopathy of prematurity, Cost-effectiveness analysis, Cost-utility analysis, Cost of illness, Cost-benefit analysis, Cost minimization analysis, Incremental cost-effectiveness ratio, Quality adjusted life years, return on investment, burden of illness, disability adjusted life years, and Economic evaluation</i>. Screening was conducted using Covidence, and the risk of bias was assessed using the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) checklist. Data extraction was performed using MS Excel. 1,527 articles were examined with nine (9) papers identified, one (1) from the UK; two (2) from Canada and six (6) from the US. Cost-effectiveness analysis was the main form of EE conducted (n = 5) and telemedicine screening (n = 3) was found to be highly cost-effective for ROP with the ICER values ranging from £446 to £4,240 per Quality Adjusted Life Year (QALY) in 2021 figures. 73% of included studies complied with the CHEERS checklist for EE. ROP screening and treatment strategies reviewed were highly cost-effective. This review may assist eye health policymakers in planning nationwide screening and treatment programs to combat vision loss and blindness due to ROP.
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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 | 0.003 |
| 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) | 0.001 | 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".