Glaucoma screening: where are we and where do we need to go?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Current recommendations for glaucoma screening are decidedly neutral. No studies have yet documented improved long-term outcomes for individuals who undergo glaucoma screening versus those who do not. Given the long duration that would be required to detect a benefit, future studies that may answer this question definitively are unlikely. Nevertheless, advances in artificial intelligence and telemedicine will lead to more effective screening at lower cost. With these new technologies, additional research is needed to determine the costs and benefits of screening for glaucoma. RECENT FINDINGS: Using optic disc photographs and/or optical coherence tomography, deep learning systems appear capable of diagnosing glaucoma more accurately than human graders. Eliminating the need for expert graders along with better technologies for remote imaging of the ocular fundus will allow for less expensive screening, which could enable screening of individuals with otherwise limited healthcare access. In India and China, where most glaucoma remains undiagnosed, glaucoma screening was recently found to be cost-effective. SUMMARY: Recent advances in artificial intelligence and telemedicine have the potential to increase the accuracy, reduce the costs, and extend the reach of screening. Further research into implementing these technologies in glaucoma screening is required.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".