Neonatal Outcomes of Infants Admitted to a Large Government Hospital in Amman, Jordan
Bibliographic record
Abstract
OBJECTIVE: To describe characteristics and outcomes of Jordanian newborns admitted to a large governmental neonatal intensive care unit (NICU). METHODS: Newborns born at the government hospital, Al Bashir, in Amman, Jordan were prospectively enrolled. The study focused on newborns admitted to the NICU and a retrospective chart review was performed. Abstraction included in-hospital mortality, antibiotic days, ventilation, oxygen use, and CRP levels. Rank sum and chi-squared tests were used to compare across outcomes. Logistic regression of hypothesized risk factors with death adjusted for gestational age. RESULTS: Of the 5,466 neonates enrolled from 2/10-2/11, medical records were available for 321/378(84.9%) infants admitted to the NICU. The median gestational age was 36 weeks, median birth weight was 2.3 kg, and 28(8.7%) infants died. The two most common reasons for admission and mortality were respiratory distress syndrome and prematurity. Low Apgar scores and positive CRP were predictors of mortality. Risk factors associated with increased use of antibiotics, oxygen hood, and mechanical ventilation included lower gestational age and prematurity. CONCLUSION: Infants admitted to the Jordanian NICU have significantly higher median gestational age and birth weights than in developed countries and were associated with significant morbidity and mortality. Continuations of global efforts to prevent prematurity are needed.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".