Clinical Findings Leading to the Diagnosis of Sepsis in Neonates Hospitalized in Imam Khomeini and Bu Ali Hospitals, Sari, Iran: 2011-2012
Bibliographic record
Abstract
BACKGROUND: One of the important diseases in neonatal period is sepsis. Clinical sign and symptoms in addition to lab tests are the most important way to accurate diagnosis and prevention of mortality. This study was conducted with the aim of determining the most clinical sign and symptoms which leading to diagnosis of sepsis. MATERIALS & METHODS: This is a descriptive cross-sectional study. The medical records of patients hospitalized in hospitals of Mazandaran University of Medical Sciences during 2011-2012 were reviewed. Variables were age, sex, birth and admission weight, clinical sign and symptoms, methods of delivery, admission and discharge condition, discharge status, the time elapsed between showing the symptom and admission to hospital, gestational age and the result of cultures. The data were recorded in a checklist and analyzed with SPSS and descriptive statistics. RESULTS: finding showed that 120 patients discharged during period of study with diagnosis of sepsis. Discharged status of 27 (%22/5) were expired. Median age was 1 day with 8 hours SD, length of stay were 12±1 days, gestational age was34±3 weeks and median birth weight was 2477±977 grams. The median time elapsed between showing the symptom and admission to hospital was 38±31 hours. Blood culture in 10 (%8/3) and urine culture in 8 (%7/6) patients were positive. None of patients have positive lumbar puncture culture. The frequent sign and symptpms in patients were respiratory distress, poor feeding and lethargy. CONCLUSION: Early diagnosis of neonatal sepsis is not possible only by specific laboratory exams. Clinical sign and symptoms can help us to prediction and diagnosis of neonatal sepsis. Results of this research revealed that it is not clear which one of manifestations was started first or the second because of medical history sheets don't show this process.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".