Systematic review: What is the impact of triage implementation on clinical outcomes and process measures in low‐ and middle‐income country emergency departments?
Bibliographic record
Abstract
INTRODUCTION: Triage is widely regarded as an essential function of emergency care (EC) systems, especially in resource-limited settings. Through a systematic search and review of the literature, we investigated the effect of triage implementation on clinical outcomes and process measures in low- and middle-income country (LMIC) emergency departments (EDs). METHODS: Structured searches were conducted using MEDLINE, CENTRAL, EMBASE, CINAHL, and Global Health. Eligible articles identified through screening and full-text review underwent risk-of-bias assessment using the Newcastle-Ottawa Scale. The quality of evidence for each effect measure was summarized using GRADE. RESULTS: Among 10,394 articles identified through the search strategy, 58 underwent full-text review and 16 were included in the final synthesis. All utilized pre-/postintervention methods and a majority were single center. Effect measures included mortality, waiting time, length of stay, admission rate, and patient satisfaction. Of these, ED mortality and time to clinician assessment were evaluated most frequently. The majority of studies using these outcomes identified a positive effect, namely a reduction in deaths and waiting time among patients presenting for EC. The quality of the evidence was moderate for these measures but low or very low for all other outcomes and process indicators. CONCLUSIONS: There is moderate quality of evidence supporting an association between the introduction of triage and a reduction in deaths and waiting time. Although the available data support the value of triage in LMIC EDs, the risk of confounding and publication bias is significant. Future studies will benefit from more rigorous research methods.
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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 | 0.054 | 0.247 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.011 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".