GAMBARAN DERAJAT NYERI DADA PASIEN PASCA TINDAKAN INTERVENSI KORONER PERKUTAN
Bibliographic record
Abstract
Penyakit Jantung Koroner (PJK) adalah penyakit yang terjadi akibat aterosklerosis sehingga arteri koronaria menyempit. Salah satu gejala dari penyakit jantung koroner adalah adanya angina, yaitu perasaan tidak nyaman dan nyeri dada. Salah satu pilihan tatalaksana penting untuk PJK, termasuk meredakan angina, adalah Intervensi Koroner Perkutan (IKP). Namun, masih ada pasien yang mengalami angina pasca IKP. Tujuan dari penelitian ini adalah mengetahui derajat nyeri dada pasien sebelum dan sesudah IKP. Penelitian ini bersifat analitik observasional dengan pendekatan cross sectional. Metode sampling yang digunakan yakni consecutive sampling dan melibatkan 88 responden. Responden penelitian adalah pasien pasca IKP yang diwawancarai di Poliklinik Jantung RSUDZA. Dari hasil penelitian diperoleh hasil sebanyak 41 responden (46,6%) mengalami angina pasca IKP sedangkan 47 responden (53,4%) tidak mengalami angina pasca IKP. Nyeri dada yang responden kemudian diukur dengan Canadian Cardiovascular Society grading of Angina Pectoris (CCSA). Hasil didapatkan bahwa sebelum tindakan pasien mengalami angina berat, yakni Grade III. Setelah IKP, jumlah responden yang mengalami angina menurun dan responden yang merasakan angina berada dalam Grade ? II. Kesimpulan dari penelitian ini adalah 46,6% pasien pasca IKP masih merasakan nyeri dada dan nyeri dada responden berkurang secara signifikan setelah menjalani IKP.Kata Kunci : Penyakit Jantung Koroner, Intervensi Koroner Perkutan, Angina Pasca IKP, Canadian Cardiovascular Society grading of Angina Pectoris
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".