Gambaran Kontrol Plak Pada Anak Stunting yang Mengalami Gangguan Kemampuan Psikomotorik
Bibliographic record
Abstract
Latar Belakang: Stunting merupakan kondisi kekurangan gizi kronis sehingga anak memiliki tubuh yang pendek ataupun sangat pendek berdasarkan indeks TB/U berdasarkan tabel WHO yang memiliki ambang batas (z-score) diatas -3 SD sampai dengan dibawah -2 SD. Stunting mampu menyebabkan penurunan tingkat kognitif dan kemampuan psikomotorik anak. Kegiatan menyikat gigi merupakan salah satu tindakan kontrol plak yang dipengaruhi oleh kemampuan psikomotorik yaitu motorik halus. Kemampuan kontrol plak anak mampu mempengaruhi indeks plak anak ke dalam kategori baik, sedang, dan buruk. \nTujuan: Mengetahu gambaran kontrol plak pada anak stunting yang mengalami gangguan kemampuan psikomotorik pada beberapa TK di wilayah kerja Puskesmas Lubuk Kilangan. \nMetode: Deskriptif observasional dengan jumlah sampel 35 anak dengan usia 4-6 tahun. Pengambilan data dilakukan dengan memeriksa kemampuan psikomotorik anak dengan DDST/Denver II untuk mendapatkan sampel sesuai kriteria inklusi. Kemudian memeriksa indeks plak PHP-M pada permukaan labial/bukal dan palatal/lingual 6 gigi tertentu. Selanjutnya melakukan observasi kemampuan kontrol plak anak dengan melihat cara anak menyikat gigi. \nHasil: Menunjukkan kemampuan kontrol plak anak stunting lebih banyak termasuk kategori buruk sebesar 80% dibandingkan kategori baik sebesar 20%. Sehingga mempengaruhi hasil indeks plak PHP-M anak cenderung termasuk kategori buruk (28.6%) hingga sedang (48.6%). \nKesimpulan: anak stunting dengan gangguan kemampuan psikomotorik memiliki kemampuan kontrol plak dan indeks plak PHP-M buruk.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".