Diagnosis Dini Isolated Posterior Myocardial Infarction (IPMI) Pada Daerah Terpencil: Laporan Kasus
Bibliographic record
Abstract
Isolated posterior myocardial infarction (IPMI) merupakan salah satu bentuk sindrom koroner akut yang cukup jarang ditemukan dibanding STEMI anterior dan inferior, dan berpotensi luput terdiagnosis karena presentasi klinis yang atipikal dan keterbatasan sadapan posterior dalam EKG standar. Seorang pasien laki-laki datang dengan keluhan nyeri punggung memberat sejak 3 hari terakhir. Nyeri terasa seperti ditusuk-tusuk dan panas, disertai keringat dingin. Pasien memiliki riwayat merokok, hipertensi tidak terkontrol dan dispepsia. Riwayat nyeri dada sudah dialami sejak 6 bulan terakhir, namun memberat dalam 3 hari terakhir dengan durasi ±1 jam tiap serangan. Hasil pemeriksaan fisik dan EKG 12 sadapan serta sadapan posterior dan kanan menunjukkan STEMI posterior terisolasi. Pemeriksaan enzim jantung tidak dapat dilakukan karena tidak tersedianya pemeriksaan tersebut. Laporan kasus ini bertujuan untuk meningkatkan kesadaran akan pentingnya deteksi dini IPMI, terutama di daerah dengan fasilitas medis yang terbatas. STEMI posterior terisolasi sering kali sulit dikenali karena gejalanya yang tidak khas dan keterbatasan pada pemeriksaan EKG standar, sehingga berisiko terlambat ditangani. Melalui laporan ini, kami ingin menekankan pentingnya penggunaan sadapan EKG tambahan serta meningkatkan kepekaan klinis terhadap IPMI, demi mencegah komplikasi yang lebih serius dan menyelamatkan lebih banyak nyawa.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.011 | 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".