TREAT-AND-EXTEND REGIMENS WITH ANTI-VEGF AGENTS IN RETINAL DISEASES
Bibliographic record
Abstract
In Brief 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. There is a lack of consensus on the definition and optimal application of treat-and-extend regimens (TERs) 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.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".