TREAT-AND-EXTEND REGIMENS WITH ANTI-VEGF AGENTS IN RETINAL DISEASES
Why this work is in the frame
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Bibliographic record
Abstract
PURPOSE: A review of treat-and-extend regimens (TERs) with intravitreal anti-vascular endothelial growth factor agents in retinal diseases. METHODS: There is a lack of consensus on the definition and optimal application of TER in clinical practice. This article describes the supporting evidence and subsequent development of a generic algorithm for TER dosing with anti-vascular endothelial growth factor agents, considering factors such as criteria for extension. RESULTS: A TER algorithm was developed; TER is defined as an individualized proactive dosing regimen usually initiated by monthly injections until a maximal clinical response is observed (frequently determined by optical coherence tomography), followed by increasing intervals between injections (and evaluations) depending on disease activity. The TER regimen has emerged as an effective approach to tailoring the dosing regimen and for reducing treatment burden (visits and injections) compared with fixed monthly dosing or monthly visits with optical coherence tomography-guided regimens (as-needed or pro re nata). It is also considered a suitable approach in many retinal diseases managed with intravitreal anti-vascular endothelial growth factor therapy, given that all eyes differ in the need for repeat injections. CONCLUSION: It is hoped that this practical review and TER algorithm will be of benefit to health care professionals interested in the management of retinal diseases.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it