Tinjauan Sistematis Hubungan Diabetes Mellitus dengan Keratoconus
Bibliographic record
Abstract
Pendahuluan: Keratoconus adalah ektasia kornea yang paling umum akibat multifaktoral yang masih banyak dipelajari. Diabetes mellitus menghambat keratoconus karena kondisi hiperglikemia yang menyebabkan glikosilasi dan cross-linking kolagen. Ada perbedaan hasil penelitian yang menunjukkan penderita keratoconus berkorelasi positif dengan diabetes mellitus. Metode: Studi ini berupa tinjauan sistematis yang membahas topik mengikuti tahapan dan protokol yang ditetapkan oleh Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020. Publikasi tahun 2014 hingga 2024 dipertimbangkan dengan memanfaatkan situs referensi online PubMed, ScienceDirect, dan SagePub. Kata kunci yang digunakan "keratoconus", “corneal ectasia”, “corneal cross-linking”, “conical cornea” dan "diabetes mellitus" juga dibantu Boolean operator. Jenis desain studi observasional dipilih, kualitas dinilai dengan skala Newcastle-Ottawa dan ditelaah kritis. Hasil: Identifikasi dilakukan dengan memasukkan kata kunci pada database PubMed memunculkan 3.478 artikel, 567 artikel di ScienceDirect, dan terdapat 108 artikel di SagePub. Kami mengumpulkan total 5 penelitian yang memenuhi kriteria dan melaporkan 6 hasil yang menjelaskan keterkaitan diabetes mellitus dengan kejadian dan keparahan keratoconus. Kesimpulan: Tidak ditemukan bukti yang cukup untuk menyatakan hubungan antara diabetes mellitus dan keratoconus, di mana masih ada kontroversi hasil dalam tinjauan ini.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".