Predicting Non-response to Ranibizumab in Patients with Neovascular Age-related Macular Degeneration
Bibliographic record
Abstract
Abstract Purpose: To validate known and determine new predictors of non-response to ranibizumab in patients with neovascular age-related macular degeneration (AMD) and to incorporate these factors into a prediction rule. METHODS: This multicenter, observational cohort study included 391 patients treated with ranibizumab for neovascular AMD. We performed genetic analysis for single nucleotide polymorphisms in AMD-associated genes and collected questionnaires regarding environmental factors and disease history. The primary outcome was non-response to treatment, defined as a loss of visual acuity >/=30% of letters. RESULTS: Of the 391 patients, 47 were classified as non-responsive. Independent predictors for non-response were age, baseline visual acuity, diabetes mellitus and accumulation of risk alleles in the CFH, ARMS2 and VEGF-A genes. The area under the receiver operating characteristic curve was 0.77 (95% confidence interval 0.70-0.84). We derived a clinical prediction rule, with possible total risk scores ranging from 0-19 points. The absolute risk of non-response varied from 3-52% between risk score groups. CONCLUSION: This is an important step towards a clinical prediction rule that can aid clinicians in identifying AMD patients with increased likelihood of non-response, and consequently contribute to making shared treatment decisions.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".