GAMBARAN EARLY CHILDHOOD CARIES (ECC) DI POSYANDU TERINTEGRASI PAUD (PENDIDIKAN ANAK USIA DINI) KECAMATAN SIJUNJUNG KABUPATEN SIJUNJUNG SUMATERA BARAT (Preliminary Study Pengembangan Surveilans ECC di Kabupaten Sijunjung Sumatera Barat pada bulan Juli 2013)
Bibliographic record
Abstract
Early Childhood Caries (ECC) atau karies pada anak usia dini merupakan masalah kesehatan masyarakat yang besar dan menjadi penyakit infeksi yang kronis pada anak yang sulit dikontrol. Belum ada data yang dapat mewakili gambaran beban penyakit ECC khususnya di Sumatera Barat lebih khusus lagi di Kabupaten Sijunjung yang dapat digunakan untuk perencanaan program dalam memecahkan masalah ECC. Studi Deskriptif cross sectional ini bertujuan untuk mendapat gambaran prevalensi, pengalaman dan tingkat keparahan Early Childhood Caries (ECC) pada anak usia 3 - 6 tahun yang akan digunakan sebagai preliminary study pengembangan surveilans ECC di Kabupaten Sijunjung Sumatera Barat. Metode yang dipakai yaitu pemeriksaan klinis dengan menggunakan indeks DMFT/dmft untuk mengukur pengalaman ECC. Indeks PUFA/pufa digunakan untuk menilai adanya kondisi oral dan infeksi akibat ECC tidak terawat. Kesimpulannya adalah prevalensi ECC dan infeksi odontogenik yang didapat menunjukkan bahwa sebagian besar kondisi kesehatan gigi dan mulut anak usia pra sekolah masih belum menjadi perhatian serius dan sebagian besar kasus karies pada anak usia dini (ECC) didapati belum dilakukan perawatan.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".