Feto-maternal outcome of placenta previa with or without placenta accreta spectrum in tertiary hospital Dhaka
Bibliographic record
Abstract
Background: Placenta previa, with or without placenta accreta spectrum (PAS), is linked to significant maternal and fetal morbidity and mortality, primarily due to complications like cesarean delivery, severe antepartum/postpartum hemorrhage and preterm birth. The combined military hospital (CMH) Dhaka, with its advanced facilities and sufficient case volume, provides an ideal environment for this research. The main objective was to evaluate the feto-maternal outcomes of placenta previa with or without PAS and develop a management framework to improve these outcomes. Methods: This retrospective study was conducted in the Department of Obstetrics and Gynecology at CMH Dhaka. The study population consisted of 99 pregnant women with placenta previa and PAS admitted between January 2023 and June 2024. Data were collected through a questionnaire, ultrasound reports, operative findings and histological reports. Results: The mean age of participants was 29.59 years. Women with a previous cesarean section had a higher risk of placenta previa (71.71%). Planned cesarean sections were performed at 34-36 weeks of gestation in 70.7% of cases. Emergency LSCS was required in 9 cases due to antepartum hemorrhage (APH), while 88 were elective cesarean sections and 2 were incidental findings during elective LSCS. Intraoperative complications included bladder injury (47 cases) and peripartum hysterectomy (61.61%). Among the newborns, 25 (25.25%) had a birth weight of 1-2 kg and 77 (77.77%) were preterm, with 5 (5.05%) being very low birth weight. APGAR scores<6 at one minute were observed in 14 babies, while 85 babies had scores>6. Conclusions: Placenta previa with or without PAS is strongly associated with serious maternal and fetal complications. Our study aims to formulate management guidelines to improve outcomes for both mother and baby.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".