Glaucoma screening: where are we and where do we need to go?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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 it