Does the Efficacy, Durability, and Safety of Faricimab for Diabetic Macular Edema in Randomized Trials Translate into Real-World Settings?
Bibliographic record
Abstract
Diabetic macular edema (DME) remains a major cause of visual impairment, with global prevalence expected to increase to around 24 million affected individuals by 2030 [ 1 ]. While intravitreal anti-vascular endothelial growth factor (VEGF) monotherapies, such as aflibercept and ranibizumab, have transformed outcomes for patients with DME, the unmet need for these patients remains high given the need for frequent injections [ 1 , 2 , 3 ]. The high treatment burden of these anti-VEGF therapies on patients, carers, and healthcare systems can lead to suboptimal long-term outcomes and reduced quality of life; more efficacious and durable therapies are needed to help alleviate this burden [ 1 , 2 , 3 ].
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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 | 0.189 | 0.344 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".