PERBANDINGAN PROFIL PENDERITA TUBERKULOSIS PARU \nANTARA PEROKOK DAN NON PEROKOK \nDI POLIKLINIK PARU RSUP. Dr. M. DJAMIL PADANG
Bibliographic record
Abstract
Tuberkulosis (TB) merupakan masalah kesehatan masyarakat yang \npenting di dunia, terutama di negara berkembang seperti Indonesia. Salah satu \nfaktor risiko yang dapat menurunkan daya tahan tubuh terhadap bakteri \nMycobacterium tuberculosis adalah faktor merokok. Menurut Public Health \nAgency of Canada terdapat hubungan yang erat antara merokok dengan TB. \nPenelitian ini bertujuan untuk mengetahui perbandingan profil penderita TB paru \nantara perokok dan non perokok di Poliklinik Paru RSUP.Dr.M.Djamil Padang. \nPenelitian ini dilakukan di Poliklinik Paru RSUP Dr. M.Djamil Padang \ndengan menggunakan desain cross sectional komparatif terhadap 44 penderita TB \nperokok dan 44 penderita TB non perokok. Data dikumpulkan melalui wawancara \ndan dari rekam medis. \nHasil uji statistik menunjukkan terdapat perbedaan yang bermakna antara \npenderita TB paru perokok dan non perokok berdasarkan hasil pemeriksaan BTA \nawal (p value : 0,012) dan gejala hemoptisis (p value : 0,002). Sedangkan, hasil \nuji statistik pada rata-rata usia (p value : 0,109), kejadian TB relaps (p value : \n0,244) dan adanya komplikasi (p value : 0,395) menunjukkan tidak ada perbedaan \nyang bermakna. \nKata kunci : tuberkulosis paru, profil, perokok
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.076 | 0.018 |
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".