Retrospective analysis of the real‐world utilization of ranibizumab in wAMD
Bibliographic record
Abstract
Abstract Purpose With monthly ranibizumab treatment for wet AMD (wAMD), optimal functional outcomes are achieved. To reduce management burdens, as‐needed dosing regimens have been explored. AURA study examines real‐world utilization of ranibizumab. Methods Retrospective, international (Canada, France, Germany, Ireland, Italy, Netherlands, UK, Venezuela), non‐interventional, observational study. Target enrollment is 444 patients per country and will be completed by September 2012. Consecutive AMD patients prescribed ranibizumab by their physicians will be included, with a follow‐up period of up to 2.5 years. Primary outcomes are (1) change in visual acuity and (2) resource utilization (number of treatment and monitoring visits, treatment use). Descriptive statistics will be used. Results Results for Germany are already available: 916 patients from 28 sites were screened. Of these, 462 (50.4%) were not enrolled. The reasons (not mutually exclusive) included no written informed consent (n=349), consent obtained after target patient number reached (n=68), use of non‐conventional interventions (n=21), ranibizumab use did not start between January‐August 2009 (n=23), and no wAMD diagnosis (n=6). Of the 454 enrolled, 437 (96.3%) received ≥1 ranibizumab dose. Of the 437, most were women (n=260, 59.5%), Caucasian (n=368, 84.2%), and initiated treatment at ≥75 years of age (n=304, 69.6%). Mean age was 79.2±8.1 years. Conclusion These data will provide valuable insight into resource utilization patterns and their effects on visual outcome in various ranibizumab‐treated wAMD patient populations. Commercial interest
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".