Bibliographic record
Abstract
Introduction: Intraventricular hemorrhage (IVH) is one of the major causes of neonatal inability and death.Due to the higher incidence of IVH in very low birth weight and preterm infants, this study aimed to determine the prevalence of Intraventricular hemorrhage in very low birth weight and premature infants admitted to the Persian Gulf Hospital.Method: This study was conducted on 350 infants with very low birth weight and prematurity admitted to the Persian Gulf Hospital from March 2017 to May 2018.Sex, gestational age, weight, maternal morbidity, familial history, complications and grade of intraventricular hemorrhage were investigated.T-test and Chi-square were used for data analysis.Results: The prevalence of intraventricular hemorrhage was 9.4% in newborns.The average weight and age of pregnancy in IVH neonates were significantly lower than those without IVH and the frequency of female subjects was significantly higher in neonates with intraventricular hemorrhage than other neonates, and other factors were not related to the neonatal disease.Conclusion: Considering the high prevalence of IVH in low birth weight infants and the identification of risk factors in this study (low birth weight, low gestational age, and female gender) and, on the other hand, mortality and morbidity of newborns with low birth weight, especially newborns with ventricular hemorrhage, routine skull ultrasonography is recommended for timely screening and risk factor modification.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.874 | 0.846 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".