ALCOHOL ATTENDANCE WITHIN THE EMERGENCY DEPARTMENT
Bibliographic record
Abstract
Objectives & Background How much does alcohol contribute to the demands on the Emergency Department (ED)? York is a popular tourist destination, particularly amongst hen and stag parties. But a quarter of the resident population have previously been identified as higher risk drinkers. So therefore, how much does alcohol contribute to the pressures on York ED? Additionally, clinical coding of alcohol within the ED is anecdotally unreliable. How true is this? We therefore undertook an alcohol needs assessment within York ED looking at general demographics, reasons for attendance and evidence of alcohol linked to the attendance. We also looked at the discrepancy between how much the ED was paid for these patients by commissioners and the actual cost to the acute trust. Methods We randomly selected 1 week per quarter in 2011 and hand searched every ED record for evidence of alcohol-related attendance. We also included patients for whom it was felt alcohol was highly likely although not directly mentioned. We undertook a concordance assessment around the alcohol question and achieved 94%. Results The 4 randomly selected weeks amounted to a 5,704 patient sample, 7.2% of the total number of attendances in 2011. 9.8% of attendances were alcohol-related (553 patients) Between 21:00 and 09:00, this rose to 19.7% Alcohol was involved in 45% of mental health attendances The alcohol group was heavily over-represented in the patients removed by police (100%), refusing treatment (55%) and leaving prior to their treatment (41%) 10.3% of alcohol-related attendees remained in the ED for >4hours compared with 5.9% of non-alcohol-related attendees 62.8% of alcohol-related attendees were living within the City of York 18% of all ambulance journeys were due to alcohol Although 553 patients had evidence of alcohol in their attendance, it was only coded as such in 46 computer records If these figures are extrapolated to cover the annual patient population, the discrepancy between what the commissioners pay and the true cost of these patients is £552,431 Conclusion Alcohol poses a disproportionate burden on York Emergency Department and Yorkshire Ambulance Service. With pressures on staffing, the 4 hour standard and ambulance turnaround times at an all-time high, how different would the ED be if the alcohol burden were reduced? This needs assessment fuels the argument for an 'invest to save' attitude to reduce alcohol-related attendance.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".