Increase in the Population of Patients with Neovascular Age-Related Macular Degeneration Who Underwent Long-Term Active Treatment
Bibliographic record
Abstract
To investigate changes in the size of the population of patients who are receiving long-term, active treatment for neovascular age-related macular degeneration (AMD). This retrospective, observational study included 18,165 patients who received anti-vascular endothelial growth factor injections (3,974 eyes). The injections performed were divided into the following three groups: group 1, injections performed right after the initial diagnosis; group 2, injections performed <24 months; and group 3, injection performed ≥24 months. Time-dependent changes in the proportion of injections in each group were analyzed. The total number of injections markedly increased from 431 in the 1st quarter of 2014 to 1,323 in the 4th quarter of 2018. There were significant changes in the proportion of injections in each group over time (P < 0.001). The proportions of group 1, group 2, and group 3 in the 1st quarter of 2014 were 17.4%, 65.4%, and 17.2%, respectively. The proportions changed to 10.6%, 50.2%, and 39.5% in the 4th quarter of 2018, respectively. The marked increase in the proportions of group 3 may suggest an increase in the patient population that underwent long-term active treatment. The socioeconomic influence of this trend should be considered when establishing future strategies for neovascular AMD.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".