Investigating the predictive validity of an emergency department mental health triage tool
Bibliographic record
Abstract
Abstract Emergency department mental health triage is a complex clinical task for which the evidence base is minimal. Research in the past decade has consistently identified issues associated with the accuracy and consistency of mental health triage assessment. In this study, we investigated the predictive validity of the clinical descriptors in the V ictorian E mergency D epartment M ental H ealth T riage T ool. Using a naturalistic, retrospective study design, an audit of the emergency department triage database was undertaken on 12 months of continuous data for all mental health presentations ( n = 1718). The main outcome measure was urgency categorization. The study findings indicate that triage nurses can accurately identify the urgency of mental health presentations using defined clinical criteria. A significant finding was that patients with acute psychotic symptoms were more likely to be triaged as high urgency (code 2); however, the A ustralasian T riage S cale, in use in all A ustralian and many international emergency departments, provides no mental health‐specific descriptors in high‐urgency categories (1 and 2).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".