HUBUNGAN TINGKAT KEPARAHAN KARIES TERHADAP KEJADIAN MALOKLUSI PADA ANAK SEKOLAH DASAR
Bibliographic record
Abstract
Latar Belakang: Maloklusi masih menjadi permasalahan gigi dan mulut di Indonesia, dengan angka masalah sebesar 80%. Jenis maloklusi yang paling sering ditemui yaitu gigi berjejal. Salah satu faktor penyebab terjadinya maloklusi adalah karies gigi. Masalah karies masih menjadi perhatian di Kalimantan Selatan, dengan prevalensi sebesar 46,9%. Kejadian karies banyak dialami pada periode gigi bercampur, yang rentan terhadap masalah kesehatan gigi dan mulut. Karies yang tidak dirawat akan mempengaruhi kestabilan oklusi normal gigi-geligi sehingga terjadinya maloklusi. Keadaan tersebut dapat menyebabkan suatu keparahan pada gigi permanen jika tidak segera dilakukan perawatan. Tujuan: Menganalisis hubungan tingkat keparahan karies terhadap kejadian maloklusi pada siswa SDN 1 Banua Hanyar di kecamatan Pandawan kabupaten Hulu Sungai Tengah. Metode: Penelitian ini menggunakan metode observasional analitik dengan pendekatan cross-sectional serta analisis data menggunakan uji korelasi Spearman. Jumlah responden adalah sebanyak 39 orang. Hasil: Tingkat keparahan karies berada pada kategori sedang, dengan rata-rata DMF-T sebesar 2,7. Kejadian maloklusi berdasarkan pengukuran Occlusal Index didapatkan kategori maloklusi sedang yang perlu perawatan minor. Hasil uji korelasi Spearman didapatkan nilai signifikansi sebesar 0,831 (>0,05). Kesimpulan: Tidak terdapat hubungan yang signifikan antara keparahan karies terhadap kejadian maloklusi pada siswa SDN 1 Banua Hanyar Kecamatan Pandawan Hulu Sungai Tengah. Kata kunci: Gigi Bercampur, Karies, Maloklusi, Occlusal Index.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".