Comparison between the Smart Triage model and the Emergency Triage Assessment and Treatment (ETAT) guidelines in triaging children presenting to the emergency departments of two public hospitals in Kenya
Bibliographic record
Abstract
Abstract Several triage systems have been developed, but little is known about their performance in low-resource settings. Evaluating and comparing novel triage systems to existing triage scales provides essential information about their added value, reliability, safety, and effectiveness before adoption. This prospective observational study included children aged < 15 years who presented to the emergency departments of two public hospitals in Kenya between February and December 2021. We compared the performance of Emergency Triage Assessment and Treatment ( ETAT ) guidelines and Smart Triage (ST) models ( ST-only model , ST model with independent triggers , and recalibrated ST model with independent triggers ) in categorizing children into emergency, priority, and non-urgent triage categories. We visualized changes in classification of participants using Sankey diagrams. 5618 children were enrolled, and the majority (3113, 55.4%) were aged between one and five years of age. Overall admission and mortality rates were 7% and 0.9%, respectively. ETAT classified less children, 513 (9.2%), into the emergency category compared to 790 (14.1%), 1163 (20.8%) and 1161 (20.7%) by the ST-only model , ST model with independent triggers and recalibrated model with independent triggers , respectively. ETAT also classified more children, 3089 (55.1%), into the non-urgent triage category compared to 2442 (43.6%), 2097 (37.4%) and 2617 (46.7%) for the respective ST models . ETAT classified 191/395 (48.4%) of admitted patients as emergency compared to more than half by all the ST models . ETAT and the ST-only model classified 25/49 (51%) children who died as emergencies, while the ST models with independent triggers classified 39/49 (79.6%) children as emergencies. Smart Triage shows potential for identifying critically ill children in low-resource settings, particularly when combined with independent triggers. Additionally, it performs comparably to ETAT . Evaluation of Smart Triage in other contexts and comparison to other triage systems is required. Author summary Prioritizing children according to the level of severity of illness in the outpatient department is crucial to ensure very sick children are identified and receive life-saving treatment while those with less severe symptoms can safely wait in the queue. Appropriate triage prevents avoidable paediatric mortality. As new triage systems are developed, it is essential to evaluate their performance before being used by healthcare professionals to manage patients. In this study, we compared a newly developed triage algorithm, Smart Triage, to the World Health Organization’s Emergency Triage Assessment and Treatment ( ETAT ) guidelines. Here, we highlight how participants were categorised into emergency, priority, and non-urgent categories by both triage systems. We also assessed changes in triage categorization by comparing the Smart Triage model only (with and without site specific recalibration) and the model with independent emergency and priority triggers aligned with ETAT . Our study shows that Smart Triage had comparable performance to ETAT, and it can be used to triage children in resource-limited settings. Smart Triage can be integrated into a digital device allowing frontline healthcare workers to rapidly triage children presenting to the outpatient department and recognize very sick children faster, so that they can be treated in a timely manner.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".