Acute Abdominal Pain Assessment in the Emergency Department: The Experience of a Greek University Hospital
Bibliographic record
Abstract
BACKGROUND: Acute abdominal pain (AAP) is a common symptom in the emergency department (ED). Because abdominal pain can be caused by a wide spectrum of underlying pathology, evaluation of abdominal pain in the ED requires a comprehensive approach, based on patient history, physical examination, laboratory tests and imaging studies. The aim of this study was to investigate predictive factors for admission to the hospital in patients who presented to the ED with AAP as the main symptom. METHODS: This prospective observational study enrolled 125 patients who presented with AAP in the ED of the Patras University Hospital in western Greece. The sample of patients who enrolled in the study was representative of patients who receive care in this academic institution. All patients underwent clinical examination, laboratory testing and radiological assessment. Clinical and laboratory data were analyzed in an attempt to identify clinical or laboratory factors predicting hospital admission. RESULTS: Based on clinical, laboratory and radiologic evaluation, 37.6% of patients enrolled in the study were admitted to the hospital, whereas 62.4% were not admitted. Compared to patients who were not admitted, patients admitted to the hospital had higher age and significantly higher inflammatory markers, white blood count and C-reactive protein (CRP). Binary logistic regression analysis showed that abnormal imaging findings (odds ratio (OR) = 6.47, 95% confidence interval (CI): 2.11 - 19.77, P < 0.001) and elevated serum CRP levels (OR = 6.24, 95% CI: 2.16 - 18.03, P < 0.001) were significant predictive factors for hospital admission. CONCLUSIONS: Assessment of AAP remains a challenging problem in the ED. Comprehensive history combined with detailed clinical examination, appropriate laboratory testing and radiologic imaging facilitates effective assessment of patients who present in the ED with AAP and guides the decision to admit patients to the hospital for further care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".