Joint Assessment of Intended and Unintended Effects of Medications: An Example Using Vascular Endothelial Growth Factor Inhibitors for Neovascular Age-Related Macular Degeneration
Bibliographic record
Abstract
Objective. To estimate the net health benefits of pegaptanib and ranibizumab by considering the impact of visual acuity and unintended effects (cardiovascular and hemorrhagic events) on quality-of-life among persons with neovascular age-related macular degeneration. Methods. We designed a probabilistic decision-analytic model using published data. It employed 17 visual health states and three for unintended effects. We calculated incremental net health benefits by subtracting the harms of each medication from the benefit using the quality-adjusted life year (QALY). Results. In a hypothetical cohort of 1,000 75-year olds with new-onset bilateral age-related macular degeneration followed for ten years, the mean QALYs per patient is 3.7 for usual care, 4.2 for pegaptanib, and 4.3 for ranibizumab. Net benefits decline with increasing baseline rates of unintended effects. Interpretation. Net health benefits present a quantitative, potentially useful tool to assist patients and ophthalmologists in balancing the benefits and harms of interventions for age-related macular degeneration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".