Emergency department of a university hospital
Bibliographic record
Abstract
OBJECTIVES: Our main aim was to describe the path of patients seen in our emergency department (ED) and either admitted or transferred and to compare the characteristics of patients hospitalized in our hospital with those of transferred patients. Our secondary aim was to compare the receipts linked to patient hospital stays. POPULATION AND METHODS: All patients seen in the ED of our hospital and ill enough to be either admitted or transferred were prospectively enrolled during 2 consecutive weeks. Information was obtained from the hospital discharge report and from local medical databases. The characteristics of the patients and receipts were compared according to their path. RESULTS: Among the 251 patients included in the study, 9% were transferred directly from the ED to another hospital. Among admitted patients, two-thirds were admitted to the short-stay unit (SSU). Schematically, patients transferred from the ED are more likely to be men around 50 years of age with few comorbidities, requiring surgery with relatively short hospital stays. Patients transferred from the SSU were more likely to be women around 67 years of age with severe comorbidities requiring medical care and longer stays. The mean receipt per day was two to three times greater for patients transferred from the ED as compared with patients hospitalized in our hospital. The mean receipt per day for patients transferred from the SSU also tended to be higher. CONCLUSION: Our results show that patients requiring shorter care are transferred, whereas more severe patients are hospitalized on site. Hospitals will need solutions to optimize their receipts while fulfilling their public missions such as continuity of care.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.090 | 0.010 |
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".