Additional file 1 of Anti-vascular endothelial growth factor therapy for age-related macular degeneration: a systematic review and network meta-analysis
Bibliographic record
Abstract
Additional file 1: Supplementary Online Content. The appendix include all supplemental data and information. eAppendix 1. Systematic Review Protocol as Registered in PROSPERO (CRD42015022041). eAppendix 2. PRISMA NMA Checklist of Items to Include When Reporting a Systematic Review Involving a Network Meta-analysis. eAppendix 3. Outcome Definitions. eAppendix 4. MEDLINE/EMBASE Literature Search Strategy. eTable 1. Recommended Dosage of Anti-VEGF Agents for Treatment of Wet AMD. eTable 2. Study Characteristics. eTable 3. Patient Characteristics. eFigure 1. Aggregate Risk of Bias Figure. eTable 4. Cochrane Risk of Bias Results for Individual Studies. eTable 5. Transitivity Assessment for all NMA Outcomes. eTable 6. All Network Meta-Analyses Results. eFigure 2. Comparison-adjusted Funnel Plots. Vision Gain. Vision Loss. Mean Change in Best-corrected Visual Acuity. Mortality. Arterial Thromboembolic Events. Adverse Events. eTable 7. All Pairwise Meta-Analysis Results. eTable 8. Sensitivity Network Meta-Analysis results. Outcome: VISION GAIN, Outcome: VISION LOSS, eTable 9. Surface Under the Cumulative Ranking Curve (SUCRA) Values for the Overall NMA and Subgroup Analyses for Vision Gain and Vision Loss. eTable 10. Surface Under the Cumulative Ranking Curve (SUCRA) Results for all Other Outcomes. eTable 11. Dose effects network meta-analysis (NMA) results. eTable 12. Confidence in Network Meta-Analysis (CINeMA) assessment for the outcome of vision gain. eTable 13. Confidence in Network Meta-Analysis (CINeMA) assessment for the outcome of vision loss. eFigure 3. Rank Heat Plot. eTable 14. Comparison to Previous Systematic Reviews.
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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 | 0.007 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.817 | 0.049 |
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".