An audit of suicide attempt admissions over a four-year period in a UK Major Trauma Centre
Bibliographic record
Abstract
Background Some admissions to UK Major Trauma Centres are due to suicide attempts. Methods An audit of suicide attempt admissions to a UK Major Trauma Centre was conducted to explore frequency and trends in admissions, demographic variables and mechanisms of injuries, referrals to and outcomes of Liaison Psychiatry during admission and whether patients had been known to mental health services prior to admission. Data were analysed retrospectively from the TARN database. Results Over a four-year period, 91 admissions due to suicide attempts were recorded. Admissions appeared to be increasing, from 1.4% in 2012 to 2.2% in 2015. Admissions were most common in males and patients in the 20–29 year age range, although admissions in the 30–39 years age range had particularly increased over the time period studied. Jumping from heights was the most common mechanism of injury, followed by self-stabbings. The majority (86%) survived their injuries. Around half of the local patients were not known to a mental health service at the time of their suicide attempt. In around a fifth who survived their injuries, no referral for a psychiatric assessment had been made. Outcomes of psychiatric assessments included referrals to mental health services, talking therapy or other support in a substantial proportion. Around a quarter was considered safe for discharge with no further mental health follow-up. Conclusions Suicide attempt admissions to UK Major Trauma Centres may be increasing, and regular audits regarding this should be undertaken. The need for appropriate psychiatric input during admission is essential, and training in mental health and suicide for staff working in MTCs is likely to be important.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".