Effectiveness of telemedicine diabetic retinopathy screening in the USA (Scopus).
Bibliographic record
Abstract
Background: Diabetic retinopathy (DR) is the leading cause of vision loss among adults in the USA. Vision loss associated with diabetic retinopathy can be prevented with timely ophthalmologic care, and therefore, it is recommended that individuals with diabetes have annual retinal examinations. There is limited evidence on whether using telemedicine to screen for DR in primary care clinics in the USA effectively leads to increased DR screening rates. The objective of this systematic review is to collate evidence from existing studies to investigate the effectiveness of telemedicine DR screening (TDRS) in primary care clinics on DR screening rates. Methods: Relevant studies will be identified through searching MEDLINE/PubMed interface, Scopus, and Web of Science from their inception until November 2021, as well as searching reference lists of included studies and previous related review articles or systematic reviews. There will be no restrictions on study design. Eligible studies will include subjects with either type 1 or type 2 diabetes, will evaluate telemedicine technology for screening of DR, will have been conducted in the USA, and will report DR screening rates or data necessary for calculating such rates. Two reviewers will screen search results independently. Risk-of-bias assessment and data extraction will be carried out by two reviewers. The version 2 of the Cochrane risk-of-bias tool (RoB 2) and the Newcastle-Ottawa scale (NOS) tool will be used to assess the quality and validity of individual studies. If feasible, we will conduct random-effects meta-analysis where appropriate. If possible, we will conduct subgroup analyses to explore potential heterogeneity sources (setting, socio-economic status, age, ethnicity, study design, outcomes). We will disseminate the findings through publications and relevant networks. Discussion: This protocol outlines the methods for systematic review and synthesis of evidence of TDRS and its effect on DR screening rates. The results will be of interest to policy makers and program managers tasked with designing and implementing evidence-based services to prevent and manage diabetes and its complications in similar settings. Systematic review registration: PROSPERO CRD42021231067. (Abstract from: https://doi.org/10.1186/s13643-023-02201-9).
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".