Identifying important questions for Cochrane systematic reviews in Eyes and Vision: Report of a priority setting exercise
Bibliographic record
Abstract
Introduction: Systematic reviews are important to inform decision-making for evidence-based health care and patient choice. Deciding which reviews should be prioritized is a key issue for decision-makers and researchers. Cochrane Eyes and Vision conducted a priority setting exercise for systematic reviews in eye health care. Methods: We established a steering group including practitioners, patient organizations, and researchers. To identify potential systematic review questions, we searched global policy reports, research prioritization exercises, guidelines, systematic review databases, and the Cochrane Library (CENTRAL). We grouped questions into separate condition lists and conducted a two-round online modified Delphi survey, including a ranking request. Participants in the survey were recruited through social media and the networks of the steering group. Results: In Round 1, 343 people ranked one or more of the condition lists. Participants were eye care practitioners (69%), researchers (37%), patients or carers (24%), research providers/funders (5%), or noneye health care practitioners (4%) and from all World Health Organization regions. Two hundred twenty-six people expressed interest in completing Round 2 and 160 of these (71%) completed the Round 2 survey. Reviews on cataract and refractive error, reviews relevant to children, and reviews on rehabilitation were considered to have an important impact on the magnitude of disease and equity. Narrative comments emphasized the need for reviews on access to eye health care, particularly for underserved groups, including people with intellectual disabilities. Conclusion: A global group of stakeholders prioritized questions on the effective and equitable delivery of services for eye health care. When considering the impact of systematic reviews in terms of reducing the burden of eye conditions, equity is clearly an important criterion to consider in priority-setting exercises.
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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 | 0.714 | 0.849 |
| Meta-epidemiology (narrow) | 0.006 | 0.009 |
| Meta-epidemiology (broad) | 0.012 | 0.023 |
| Bibliometrics | 0.034 | 0.031 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.018 | 0.023 |
| Open science | 0.009 | 0.024 |
| Research integrity | 0.024 | 0.015 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".