ANALISIS FAKTOR RISIKO KEJADIAN TUBERKULOSIS PARU
Bibliographic record
Abstract
Latar Belakang: Tuberkulosis (TB) merupakan penyakit yang masih menjadi masalah utama kesehatan secara global di dunia dan menyebabkan tingkat morbiditas pada jutaan orang setiap tahunnya. Provinsi Jawa Timur memiliki kasus TB terbanyak kedua pada tahun 2011 dengan kasus mencapai 41.404.Peningkatan infeksi TB tidak luput dari berbagai faktor, yaitu usia, jenis kelamin, status gizi, tingkat kebersihan, ventilasi, suhu, pencahayaan, kepadatan penghuni dan pendidikanTujuan:Mengetahui pengaruh faktor-faktor resiko tehadap kejadian tuberkulosis paru di wilayah Puskesmas Pesantren II Kota Kediri Metode: Menggunakan metode campuran antara kualitatif melalui Focused Group Discussion (FGD) dan kuantitatif,secara observasional analitik dengan desain studi case control. Pengambilan sampel dengan teknik total sampling. Jumlah sampel kasus 33 orang dan sampel kontrol 33 orang.Hasil Penelitian: Hasil uji regresi logistik biner menunjukkan bahwa terdapat delapan variabel yang mempunyai pengaruh signifikan terhadap kejadian TB paru, yaitu BMI (p = 0,002; OR = 8,785; CI = 1,153-66,93), tingkat pendidikan (p = 0,0026 OR = 2,944; CI = 0,183-47,29 ), riwayat imunisasi BCG (p = 0,001; OR = 0,048; CI =0,002-1,308), riwayat kontak dengan penderita TB (p = 0,004; OR = 13,269;CI = 0,737-238,96), ventilasi (p = 0,000; OR = 0,041; CI =0,001-1,432), kepadatan hunian (p = 0,000; OR = 0,113; CI 0,001-1,301), sumber air (p = 0,03; OR = 9,143; CI = 0,273-306,7), dan riwayat merokok (p = 0,000; OR = 11,706; CI = 0,746-183,66). Nilai adjusted R square menunjukkan bahwa faktor tersebut berpengaruh terhadap kejadian TB paru sebesar 85,9%. Sedangkan faktor yang paling dominan berpengaruh terhadap kejadian TB paru adalah BMI. Kesimpulan:Faktor resiko yang mempengaruhi tingkat kejadian TB meliputi BMI, tingkat pendidikan, riwayat imunisasi BCG, riwayat kontak dengan penderita TB, ventilasi, kepadatan hunian, sumber air dan riwayat merokok.Kata Kunci: TB paru, faktor resiko
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".