Life After AREDS 2: What Should We Recommend to Patients With or at Risk of AMD?
Bibliographic record
Abstract
Purpose: To establish a consensus on clinical recommendation of oral supplementation for patients with or at risk of developing age-related macular degeneration (AmD), from the perspective of the Age-Related eye Disease Study 2 (AReDS 2) and other studies. Methods: Panel discussion based on a literature review of pertinent articles related to the prevention of AmD with oral supplementation. Results: on the basis of the findings, patients must first be encouraged to modify their diet and to eliminate modifiable risk factors before being recommended any type of oral supplementation. Then, recommendations must be customized on the basis of a patient’s individual risk profile (i.e., age, gender, heredity, etc.) and severity of disease (i.e., category 1 to 4). essential fatty acids (omega-3s) and vitamins may play a role, in a given clinical population, to prevent the occurrence or the progression of AmD disease. However, there is no single formula that can be applied to all patients with or at risk of AmD. Conclusions: This group concluded that the full body of literature must be taken into consideration in order to justify clinical recommendations for patients. A single study such as AReDS 2 cannot, by itself, guide clinical practice. In all cases, recommendations must be individualized and patients should be monitored regularly.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.008 | 0.008 |
| 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".