Criteria for choosing clinically effective glaucoma treatment: A discussion panel consensus
Bibliographic record
Abstract
UNLABELLED: Abstract. BACKGROUND: In the clinical management of patients at risk for or diagnosed with primary open-angle glaucoma (POAG), the aim of medical treatment is to reduce intraocular pressure (IOP) and then maintain it over time at a level that preserves both the structure and function of the optic nerve. OBJECTIVE: The objective of this report was to establish a consensus on the criteria that should be used to determine the characteristics of IOP-lowering medication. METHODS: Discussion was held among a panel of 12 physicians considered to be experts in glaucoma to develop a consensus on the criteria used by them to determine the characteristics of the IOP-lowering medication chosen for initial monotherapy and adjunctive treatment of ocular hypertension (OHT) or POAG. Consensus development combined available evidence and the impressions of these physicians regarding the clinical effectiveness of IOP-lowering medication for OHT and POAG. Once the panel identified the criteria, the order of priority and the relative importance of these criteria were then established in the setting of 3 risk categories (low, medium, and high) for a patient to experience significant visual disability from glaucoma over their expected life span. RESULTS: The panel identified 5 criteria to determine the characteristics of IOP-lowering medication for OHT and POAG: IOP-lowering effect, systemic adverse events (AEs), ocular tolerability, compliance/administration, and cost of treatment. IOP-lowering effect was consistently ranked as the highest priority and cost as the lowest. The priority of compliance/administration did not vary by clinical situation. Systemic AEs and ocular tolerability were ranked as higher priorities in initial monotherapy than in adjunctive treatment and ranked lower as the risk for visual disability increased. The priority given to the criteria used to determine clinical effectiveness varied both with the risk for functional vision loss from glaucoma and whether initial monotherapy or adjunctive treatment was being considered. CONCLUSION: Glaucoma treatment should be assessed with regard to the need not only to lower IOP but also to minimize systemic and ocular AEs, promote patient compliance, and minimize cost. The order of priority and relative importance given to these treatment criteria will vary as part of individualizing patient care.
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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 | 0.270 | 0.246 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.012 | 0.017 |
| Research integrity | 0.019 | 0.020 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".