Reduced overtriage and undertriage with a new triage system in an urban accident and emergency department in Botswana: a cohort study
Bibliographic record
Abstract
BACKGROUND: Improvements in triage have demonstrated improved clinical outcomes in resource-limited settings. In 2009, the Accident and Emergency (A&E) Department at the Princess Marina Hospital (PMH) in Botswana identified the need for a more objective triage system and adapted the South African Triage Scale to create the PMH A&E Triage Scale (PATS). AIM: The primary purpose was to compare the undertriage and overtriage rates in the PATS and pre-PATS study periods. METHODS: Data were collected from 5 April 2010 to 1 May 2011 for the PATS and compared with a database of patients triaged from 1 October 2009 to 24 March 2010 for the pre-PATS. Data included patient disposition outcomes, demographics and triage level assignments. RESULTS: 14 706 (pre-PATS) and 25 243 (PATS) patient visits were reviewed. Overall, overtriage rates improved from 53% (pre-PATS) to 38% (PATS) (p<0.001); likewise, undertriage rates improved from 47% (pre-PATS) to 16% (PATS) (p<0.001). Statistically significant decreases in both rates were found when paediatric and adult cases were analysed separately. PATS was more predictive of inpatient admission, Intensive Care Unit (ICU) admission and death rates in the A&E than was the pre-PATS. The lowest acuity category of each system had a 0.6% (pre-PATS) and 0% (PATS) chance of death in the A&E or ICU admission (p<0.001). No change in death rate was seen between the pre-PATS and PATS, but ICU admission rates decreased from 0.35% to 0.06% (p<0.001). CONCLUSIONS: PATS is a more predictive triage system than pre-PATS as evidenced by improved overtriage, undertriage and patient severity predictability across triage levels.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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".