Faktor-faktor yang Mempengaruhi Keterlambatan Pasien Stroke Akut Datang ke Lima Rumah Sakit Pemerintah di DKI Jakarta
Bibliographic record
Abstract
Bahaya penyakit stroke umumnya belum disadari secara luas oleh masyarakat sehingga mengakibatkan kecacatan dan kematian cukup tinggi baik di Indonesia. Disain penelitian potong lintang dengan responden pasien stroke (iskemik maupun hemoragik), serangan pertama maupun berulang yang datang ke lima rumah sakit pemerintah di lima wilayah di DKI Jakarta. Sebanyak 110 subyek digunakan dalam penelitian dan faktor-faktor yang diamati adalah sebaran waktu kedatangan, usia, jenis kelamin, pendidikan, suku, pengetahuan tentang stroke, gejala klinis, jarak tempat tinggal dengan rumah sakit, tingkat pendapatan, pembiayaan. Karakteristik penderita stroke di rumah sakit pemerintah di lima wilayah DKI Jakarta mayoritas usia produktif, laki-laki, tingkat pendidikan rendah, berpendapatan rendah, umumnya berasal dari suku jawa dan lamanya waktu kedatangan ke rumah sakit lebih dari tiga jam. Faktor yang mempercepat kedatangan adalah jarak antara rumah pasien ke rumah sakit.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 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".