Utilization of Emergency Service of Obstetrics and Gynecology: A Cross-Sectional Analysis of a Training Hospital
Bibliographic record
Abstract
BACKGROUND: Overutilization and inappropriate use of emergency departments (EDs) by patients with non-urgent health problems has become a major concern worldwide. This study aims to describe the characteristics of obstetric and gynecologic patients admitted to the Department of Emergency Obstetric and Gynecology. METHODS: A retrospective and cross-sectional study was designed at our Emergency Service of Obstetrics and Gynecology of Kayseri Education and Research Hospital of Medicine between January 1 and December 31, 2013. A total of 30,853 patients applying to emergency service were retrospectively analyzed from the admission charts, patient files and hospital automation system. Patients were assessed in terms of demographic features, presentation times, complaints, admission type (with own facilities, with consultation or with ambulance), diagnoses (urgent or non-ergent), discharge rates, clinical admission, rejection rate of examination, and rejection rate of hospitalization. RESULTS: A total of 30,853 patients were analyzed retrospectively. The mean age of patients was 27.69 ± 8.44 years; 51% of patients were between 20 and 29 years old. The categories of patients in urgent and non-urgent were 69% and 31% respectively. Most common presentation time period was between 19:00 and 21:00. Labor pain, pain and bleeding during pregnency, routine antenatal control, pelvic pain and menstrual irregularity were the most common complaints. Labor pain with the rate of 21% was the most common cause of ED admission. All patients who presented with labor pain were hospitalized. Patients hospitalized for labor constituted 56% of all hospitalized patients. Among patients, 62% were treated on an outpatient basis and 38% were hospitalized. Of patients, 3.54% refused to hospitalization. The cases using the ambulance to admission constituted 1.07% of all ED patients. Of these patients who have used ambulance 3.65% refused to the patient examination. CONCLUSIONS: To improve the obstetric and gynecologic emergency medical care in Turkey, demographic properties and other characteristics of patients should be analyzed in detail. Detailed analysis of the data contributes to the further design and perspective of the EDs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".