Perbandingan Efektivitas Injeksi Agen-Agen Anti-Vegf pada Pengobatan Age-Related Macular Degeneration
Bibliographic record
Abstract
Age-related Macular Degeneration (AMD) adalah salah satu penyebab utama kebutaan pada orang > 60 tahun di negara-negara maju. Patogenesis AMD neovaskular melibatkan peningkatan permeabilitas pembuluh darah koroid, yang menyebabkan hipoksia dan produksi faktor pro-inflamasi dan pro-angiogenik, terutama faktor pertumbuhan endotel pembuluh darah (VEGF). Pengobatan AMD neovaskular melibatkan terapi anti-VEGF, yang menghambat pertumbuhan pembuluh darah yang tidak normal. Sejumlah penelitian telah menunjukkan efektivitas dan efek samping dari masing-masing agen Anti-VEGF. Berdasarkan latar belakang ini, Penulis tertarik untuk melakukan tinjauan pustaka yang merangkum efektivitas dan efek samping dari masing-masing agen Anti-VEGF. Metode yang digunakan adalah Literature Review. Jurnal di kumpulkan menggunakan GoogleScholar atau Google Cendekia, PubMed dan Proquest dalam jangka waktu 10 tahun terakhir. Sebanyak 10 jurnal yang memenuhi kriteria inklusi-eksklusi untuk dikaji dan diuji kelayakan dengan Ottawa score. Hasil dari tinjauan pustaka ini menunjukkan agen-agen anti-VEGF termasuk bevacizumab, ranibizumab, aflibercept, pegaptanib sodium, brolucizumab, dan abicipar pegol telah terbukti sama-sama efektif dalam meningkatkan penglihatan dan mempertahankan penglihatan stabil pasien AMD neovaskular. Namun, pengobatan anti-VEGF ini juga dapat menyebabkan efek samping seperti peradangan intraokular, peningkatan tekanan intraokular, dan masalah pembuluh darah.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".