MEDICAL TRIAGE SYSTEMS BASIC APPLICATION PRINCIPLES AND GLOBAL PERSPECTIVE
Bibliographic record
Abstract
Medical triage is the process of classifying patients according to their clinical priorities to maximize the efficient use of limited resources in emergency healthcare. Historically, it originated in the Napoleonic Wars and is now widely used in modern emergency services, disaster management, pandemics, and wartime situations. The primary objectives of triage are the efficient use of resources, ensuring patient safety, and ensuring the continuity of healthcare services. Triage systems are used worldwide (color-coded systems, ESI, START, CTAS), the fundamental approach remains the same: rapid identification of critically ill patients and appropriate intervention. While the START triage system is generally used in Turkey, five-step systems are preferred in hospital emergency departments in countries such as the US, Canada, and the UK. The effectiveness of triage depends on the practitioner's knowledge, experience, and ethical awareness; misclassifications can increase mortality or waste resources. With the COVID-19 pandemic, digitally supported triage systems have become a hot topic, and teletriage and automated prioritization applications have reduced hospital admission burdens. In the future, the success of triage will be strengthened by international standardization, ethics-based training, artificial intelligence-supported decision-making systems, and data security principles, contributing to both efficiency and equity in healthcare.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".