Predicting ICU Admissions from Attempted Suicide Presentations at an Emergency Department in Central Queensland, Australia
Bibliographic record
Abstract
BackgroundEmergency medicine physicians and psychiatric staff face a challenging job in risk stratifying patients presenting with suicide attempts to predict which patients need intensive care unit admission, hospital admission or can be discharged with psychiatry follow up. AimsThis study aims to analyse patients who were admitted to the intensive care unit or regular ward for suicide attempt, and the methods they employed in a rural Australian base hospital. MethodWe conducted a retrospective analysis of patients who presented with suicide attempts to the Rockhampton Base Hospital Emergency Department, Queensland Australia from 1 September 2007 to 31 August 2009.Multivariate logistic regression was undertaken to identify risk factors for ICU and regular ward admission, and predictors of suicide method. ResultsThere were 570 patients presenting with suicide attempts, 74 of which were repeat suicide attempts.There was a 10fold increase in the odds of intensive care unit or ICU admission (CI 1.45-81.9,p=0.02) for patients who presented with drug overdose.Increased age (OR=1.02,95 per cent CI 1.00-1.03,p=0.05), drug overdose (OR=2.69,95 per cent CI 1.37-5.29,p=0.004), and previous suicide attempt (OR=1.53,95 per cent CI 1.03-2.28,p=0.03) were significantly correlated with hospital admission.Male patients (OR=2.76,95 per cent CI 1.43-5.30,p=0.002) and Aboriginal patients (OR=3.38,95 per cent CI 1.42-8.05,p=0.006) were more likely to choose hanging as a suicide method.Conclusion We identified drug overdose as a strong predictor of ICU admission, while age, drug overdose and history of previous suicide attempts predict hospital admission.We recommend reviewing physician practices, especially safe medication, in suicide risk patients.Our study also highlights the need for continued close collaboration by acute care and community mental health providers for quality improvement.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".