Quick diagnostic unit integrated in an emergency department setting reduces medical admissions – an observational study
Bibliographic record
Abstract
Background: Hospitals in countries with public health systems have recently adopted organizational changes to improve efficiency and resource allocation, and reducing inappropriate hospitalizations has been established as an important goal, as well as avoiding or buffering overcrowding in Emergency Departments (EDs). Aims: Our goal was to describe the impact of a Quick Diagnostic Unit established on January 1, 2012, integrated in an ED setting in a Danish public university hospital following its function for the first year. Design: Observational, descriptive and comparative study. Methods: Our sample comprised the total number of patients being admitted and discharged from the Department of Internal Medicine in 2011 and 2012, with special focus on the General Medicine Ward. Results: Compared with 2011 the establishment of the Quick Diagnostic Unit integrated in the Emergency Department resulted in the admittance and discharge of fewer patients (40%; p < .0001) to the hospital’s General Medicine Ward and 11.6% (p < .0001) fewer patients in the whole Department of Internal Medicine. Conclusions: A Quick Diagnostic Unit integrated in an ED setting represents a useful and fast track model for the diagnostic study and treatment of patients with simple internal medicine ailments, and also serves as a buffer for overcrowding of the ED.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.003 | 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".