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 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".