Screening, Diagnosis, and Management of Open Angle Glaucoma
Bibliographic record
Abstract
Glaucoma is the most common form of irreversible blindness in the world, and second only to cataract among all causes of blindness. There is still no universally agreed-upon definition of glaucoma, and as such, it remains a condition for which there are differing views on the classification of individuals within the continuum of suspicion through diagnosis. Regardless, there appears to be consensus that glaucoma refers to a group of diseases that manifest as a characteristic progressive optic neuropathy and retinal ganglion cell loss that eventually leads to a permanent loss of visual field. Glaucoma is a major public health issue because individuals are typically asymptomatic until end stages of the disease when the associated vision loss is significant and irreversible. Studies have shown that the prevalence of undetected glaucoma is as high as 50% even in high income areas including North America and Australia, increasing to 90% in middle and low income areas such as Asia and Africa. This is at least in part a result of inadequate screening tools and strategies to detect this asymptomatic disease: without more individuals accessing routine eye examinations, glaucoma will continue to go undetected. Vision loss from glaucoma imposes significant societal and economic burdens that increase with disease severity: the direct costs of vision loss from glaucoma exceed $300 million annually in Canada, and approach $2 billion across North America.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".