Evaluation of maternal and neonatal outcomes in the case of preterm premature rupture of membranes and their relationship to prenatal maternal indicators: Across -sectional descriptive study
Bibliographic record
Abstract
Background and aim: Preterm premature rupture of membranes is one of the most important causes of pregnancy complication and a significant role in the occurrence of perinatal morbidity and mortality. The present study aims to evaluate the maternal and neonatal outcomes in the case of preterm premature rupture of membranes and their relationship to prenatal maternal indicators.Subjects and methods: A cross-sectional descriptive design was used to evaluate 68 pregnant women with a gestational age of 32 to before 37 weeks, and singleton pregnancy complicated by preterm premature rupture of membranes who fulfilled the inclusion criteria. The data were collected by convenience sampling using standardized tools.Results: A linear correlation was used to show a correlation between maternal clinical indicators with the predictive maternal and neonatal outcome using a Spearman Rho correlation coefficient. The most significant neonatal outcomes are neonatal intensive care unit admission, neonatal respiratory distress syndrome, and early neonatal sepsis. More than two-thirds of the studied women had expectant management, and less than one-fourth of them have postnatal sepsis.Conclusion and recommendation: The prenatal maternal indicators are the significant values for maternal and neonatal outcome in case of preterm premature rupture of membranes, so A further larger prospective study is recommended to demonstrate the difference in incidence, management protocol of preterm premature rupture of the membranes in the delivery and maternity health care services.
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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 | 0.002 | 0.004 |
| 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 | 0.000 |
| 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".