Assessment of Pre-Alert Compliance with National Pre-Alert Guidelines for Pre-Alerted Patients to the Emergency Department: A Cross-Sectional Study at University Hospital Waterford, Ireland
Bibliographic record
Abstract
Introduction: Emergency departments (ED) are notified by the ambulance clinicians before their arrival; this notification is called a “pre-alert”. Studies have shown that pre-alerting EDs for patients requiring time critical treatment have shown to improve initiation of early treatment and patient outcomes. appropriate use of pre-alerts can help to improve patient care and outcome. This study was conducted to determine the compliance of pre-alerts with pre-alert guidelines.Methodology: This was a cross-sectional study conducted in the ED of University Hospital Waterford and data for 71 pre-alerts was evaluated. Data was collected via a pre-existing proforma. All the pre-alerts were evaluated against National pre-alert guidelines to see whether they were rightly meeting the pre-alert criteria or were potentially inappropriately pre-alerted. IBM SPSS V.20 was used to analyse the data.Results: In the study population 38(53.5%) were males and the mean age of the patient was 63.28±25.1 years. The most common reason for pre-alert to the ED was stroke accounting for 31% of the pre-alerts. 16.9% of the pre-alerts were for patients with breathing problems. Analysis showed that 62(87.3%) of the requests had one or more than one criterion meeting the standards for pre-alert and hence were rightly pre-alerted, while 9(12.7%) pre-alerts were not meeting any physiological or diagnostic criteria for pre-alert.Conclusion: This study demonstrates overall good compliance with the majority of pre-alerts in keeping with guidelines. However, almost a quarter of pre-alerts were not meeting clinical or diagnostic criteria for pre-alerts research is required to ascertain the reasons behind these calls.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".