Chronic open-angle glaucoma. Review for primary care physicians.
Bibliographic record
Abstract
INTRODUCTION: Chronic open-angle glaucoma (COAG) is a leading cause of irreversible blindness worldwide, including in Canada. It presents a challenge in diagnosis, as disease often progresses without symptoms; an estimated 50% of cases are undetected. SOURCES OF INFORMATION: MEDLINE searches, reference lists of articles, and expert knowledge from one of the authors (K.F.D.), a glaucoma specialist, were used. MAIN MESSAGE: A casefinding approach using early referral to optometrists and ophthalmologists for early detection of COAG is helpful for patients with risk factors such as age above 50, a positive family history, black race, and myopia. Moderate evidence for referral also exists for the following risk factors: hypertension, type 2 diabetes mellitus, hypothyroidism, and sleep apnea. Treatment with intraocular pressure-lowering medication can arrest or slow the course of the disease, permitting patients to retain good visual function. Family physicians should be aware that some intraocular pressure-lowering medications, particularly topical beta-blockers, can pose iatrogenic harm to patients and result in or exacerbate such conditions as asthma, cardiovascular disturbances, depression, and sexual dysfunction. CONCLUSION: Appropriate referral patterns and an understanding of common as well as serious side effects of glaucoma medications are important in optimizing management of patients at risk of developing, or who have, COAG.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.014 |
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".