World Glaucoma Day, 6 March 2008: Tackling Glaucoma Internationally
Bibliographic record
Abstract
© 2007 Scientific Communications International Limited Correspondence: Dr George Lambrou, Athens Institute of Ophthalmology, Agias Barbaras 61, Halandri 15231, Athens, Greece. Tel: (30 210) 722 2722; Fax: (30 210) 722 2747; E-mail: gnlambrou@hotmail.com, george@lambrou.eu We have all heard many times that “Glaucoma is the second most common treatable cause of blindness worldwide”. Glaucoma is a significant public health concern, being the leading cause of irreversible blindness and consistently ranking among the leading causes of blindness in virtually every nation. In developing countries, cataract is the leading cause of blindness. In developed countries, the leading cause of blindness is age-related macular degeneration (AMD). However, there is a fundamental difference between these 2 diseases and glaucoma; their high rank as causes of blindness is due to structural reasons that are hard to address (limited access to surgical infrastructure for cataract and lack of an effective preventive treatment for AMD), whereas in the case of glaucoma, the main reason is low awareness of the disease and its implications. Indeed, it is estimated that only 50% of those affected with glaucoma in developed nations are aware that they have the disease, while as many as 90% or more of people with glaucoma in underdeveloped countries are unaware of having the disease or have even heard of glaucoma. Despite our better understanding of risk factors for glaucoma, we have yet to see an improvement in these numbers. Worse, although glaucoma occurs in all age groups, it is more common in older adults, and with an ageing population, estimates of glaucoma prevalence are increasing. It has been predicted that by 2020, 79.6 million people worldwide will have glaucoma, 11.2 million of whom will be bilaterally blind, up from the current 4.5 million. Recent years have seen considerable progress in the diagnosis and treatment of glaucoma. Technological advances in optic nerve and retinal nerve fibre layer imaging and visual field testing make it possible to diagnose glaucoma at earlier stages, when treatment has a better prognosis. Medical treatment is available and effective for controlling glaucoma for most patients, while for those who have uncontrolled disease, laser and surgical interventions are often successful. Optic nerve and visual field damage are irreversible. As damage progresses gradually, often unnoticed by the patient, early detection and treatment are of paramount importance to prevent blindness. For individuals with known risk factors for glaucoma, particularly elevated intraocular pressure, increasing age, African descent, family history of glaucoma, vasospasm, low blood pressure, and high myopia, the importance of routine examinations cannot be understated. Also, despite strong evidence that lowering intraocular pressure can delay the onset and progression of glaucoma, reported rates of non-compliance with glaucoma therapy range from 5% to as high as 80%. This high variability results from different definitions for non-compliance and the way it is measured.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.079 | 0.025 |
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".