Triage gap? Analysis of admission rates, service utilisation and mortality for First Nations patients compared to non‐First Nations patients, stratified by ED triage category
Bibliographic record
Abstract
BACKGROUND: First Nations patients often experience poorer health outcomes than non-First Nations patients. Despite emergency triage primarily focusing on severity, implying comparable outcomes for patients in the same triage group regardless of demographics, the precision of triage for First-Nations Australians may be undermined by multiple factors, although research in this area is scarce. OBJECTIVE: To compare admission rates, service utilisation and mortality for First Nations and non-First Nations patients, based on their triage categories. METHODS: This retrospective cohort study utilised data for all adults presenting between January 2016 and May 2021, to Alice Springs Hospital; totalling 175 199 presentations from 39 882 individual patients. Data were analysed for differences between First Nations and non-First nations patients for outcomes including 30-day mortality, admission to hospital and admission to ICU. RESULTS: First Nations patients had significantly higher admission than non-First Nations patients across all triage categories (P < 0.001). First Nations patients in categories 3 and 4 had a significantly higher 30-day mortality (P = 0.039, P = 0.045, respectively). First Nations patients in categories 2 and 3 were significantly more likely to be admitted to ICU (P < 0.001). CONCLUSION: First Nations patients appear to have worse outcomes than non-First Nations patients in the same triage category. Socio-economic factors and high discharge against advice rates from wards may explain the significantly higher admission rate. Under-recognition of serious illness at triage could be attributed to communication issues or a 'well bias'. The results raise many questions and further investigation is required.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".