RELIABILITAS SISTEM TRIASE DALAM PELAYANAN GAWAT DARURAT : A REVIEW
Bibliographic record
Abstract
Pendahuluan.Triase dalam pelayanan gawat darurat adalah proses pengambilan keputusan yang diterapkan untuk mengidentifikasi pasien dan mengoptimalkan sumber daya. Sejak 1990 hingga tahun 2000 telah dirancang triase 5 skala diantaranya: Australian Triage Scale (ATS), Canadian Emergency Department Triage and Acuity Scale (CTAS), Manchester Triage Scale (MTS), dan Emergency Severity Index (ESI). Sehingga timbul pertanyaan tentang keandalan skala triase (reliabilitas). Pada review kali ini penulis akan fokus pada berbagai macam skala triase, penggunaannya di beberapa negara, dan reliabilitasnya. Penulis tidak akan menulis kembali pedoman/guideline dari triase yang telah secara resmi terpublikasikan. Metode. Penelusuran ini dilakukan mulai dari Januari hingga Maret 2019 dengan melakukan penelusuran database: PubMed, EMBASE, dan CINAHL. Kata kunci yang digunakan: ‘Triage in emergency 'ATAU' Canadian Triage and Acuity Scale’ ATAU ‘Emergency Severity Index’ ATAU ‘Manchester Triage Scale’ ATAU ‘Australasian Triage Scale’. Kriteria inklusi: uji acak terkendali (randomized controlled tria), studi retrospektif, observasional, studi kasus, review, systematc review, dan meta analisis. Hasil akhir review menemukan 12 artikel yang sesuai Hasil. Uji statistik Kappa menunjukan reliabilitas ATS 0,428 (95% CI 0,340-0,509), reliabilitas CTAS 0,871 (95% CI (0,840-0,897), reliabilitas ESI 0.730 (95% CI : 0.692 hingga 0.767), reliabilitas MTS 0,751 (CI 95%: 0,677 hingga 0,810). Kesimpulan. Berdasarkan hasil reliabilitas nilai ATS menunjukan realibilitas terkecil, realibilitas CTAS merupakan yang tertinggi, namun memiliki keterbatasan pada pelaksaan diluar Kanada. MTS merupakan skala yang reliabilitas dan juga penerapannya dianggap yang paling baik. Kata Kuci: Reliabilitas, Canadian Triage and Acuity Scale (CTAAS), Emergency Severity Index (ESI), Manchester Triage Scale (MTS), Australasian Triage Scale (ATS).
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".