The System of Intraocular Pressure Assessment Using Interference Eye Pictures
Bibliographic record
Abstract
Introduction.According to the World Health Organization (WHO), glaucoma accounts for 4-5% of the total ocular pathology, making it one of the most common eye diseases in the world.The first sign of the disease is a constant or periodic increase in intraocular pressure, which leads to the development of visual field defects, optic nerve atrophy, and dystrophic changes in eye tissues.Detection of glaucoma and ocular hypertension is done by measuring the intraocular pressure, which is the standard procedure for diagnosis of the condition of eyes in all patients over 40 years of age.Patients with a diagnosis of "glaucoma" should constantly measure the intraocular pressure, which is necessary to control the effectiveness of treatment, its correction and evaluation of the effectiveness of drugs.The purpose of the article is to develop the system for assessing the intraocular pressure level using the interference pictures parameters observed on the eye cornea in the polarized light.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".