Manajemen Asuhan Kebidanan Antenatal pada Ny. J dengan Hiperemesis Gravidarum Tingkat II
Bibliographic record
Abstract
Menurut World Health Organization (WHO) jumlah kejadian hiperemesis gravidarum mencapai 12,5 % dari jumlah seluruh kehamilan di dunia dengan angka kejadian yang beragam mulai dari 0,3% di Swedia, 0,5% di California, 0,8% di Canada, 10,8% di China, 0.9% di Norwegia, 2,2% di Pakistan dan 1,9% di Turki, serta di Amerika serikat, prevalensi hiperemesis gravidarum adalah 0,5-2%. Sedangkan angka kejadian hiperemesisis gravidarum di Indonesia adalah mualai dari 1-3% dan seluruh kehamilan (Masruroh R, 2016).Tujuan dilakukannya penelitian ini adalah untuk mengetahui tingat hiperemesis gravidarum yang dirasakan Ny”J” di RS Bhayangkara Makasasar tahun 2019. Jenis penelitian ini bersifat deskriptif dengan menggunakan metode studi kasus Manajemen Kebidanan yang terdiri dari 7 langkah Varney, yaitu : Pengumpulan Data Dasar, Interpretasi Data Dasar, Diagnosa Potensial, Tindakan Segera, Menyusun Rencana, Melaksanakan Secara Menyeluruh Asuhan Kebidanan serta Mengevaluasi Keberhasilannya.Dari kasus Ny”J” yaitu Hiperemesis Gravidarum Tingkat II belum teratasi namun ibu dapat beradaptasi dengan keadaanya saat ini, tidak terdapat tanda-tanda hiperemesis gravidarum tingkat III.Penelitian ini, bidan dapat menerapkan manajemen asuhan kebidanan sesuai dengan prioritas masalah pasien secara menyeluruh sehingga tindakan yang akan dilakukan bidan dapat dipertanggung jawabkan berdasarkan metode ilmiah. Kata kunci : Antenatal care;hiperemesisi gravidarum tingkat II.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.006 |
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".