Triage for low income countries: is ESI truly the way forward?
Bibliographic record
Abstract
A triage desk at the doorstep of an emergency department (ED) is to “sort, select or prioritize” presenting patients as per their clinical needs. Many triage systems exist globally, however, the need and/or practical applicability of any triage is dictated by the hospital system and setting. In low-income/developing countries, the triage system must be capable and proficient enough to pair the right patient with the most appropriate management. Ineffective and/or in-efficient triage leads to overcrowding, delays, inappropriate resource utilization and patient dissatisfaction. A sizeable proportion of triage systems rely on three to five levels/tiers. Five level triage systems, such as the Australian Triage System (ATS) and the Canadian Triage Acuity Scale (CTAS), to name a few, are widely used worldwide. Based on door-to-physician time, these systems not only allow the institution to monitor and meet the timelines recommended by the institution policies, but have also been identified as an effective triage tool hence widely adopted in hospitals of developed countries. However, both ATS and CTAS are time-consuming and require skilled and qualified nursing staff to process it. On the other hand, the ESI (Emergency Severity Index) scale which is also a 5-level triage system, categorizes patients based on resource requirement and severity of the patient’s condition. Although ESI is in the developing phase, it is proving to be nurse-friendly and reliable in both intra and inter-rated conditions. The aim of this paper is to critically analyze the merits and pitfalls of the ESI system, in addition to proposing further modifications, in order to fulfill the needs of a developing country.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".