The Association of the Mode of Delivery and Maternal Postpartum Readmission: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background: Postpartum readmission is one of the metrics for assessing the quality of health services. There is inconsistent evidence about postpartum readmission and complications by mode of delivery. Objective: We applied a meta-analysis to investigate whether the mode of delivery is associated with postpartum readmission or complications. Methods: We systematically searched main English databases for studies published up to June 2022. The Newcastle–Ottawa scale (NOS) was used to assess the quality of observational studies. The heterogeneity of included studies was determined using the I2 statistic and Begg’s and Egger’s tests for publication bias, respectively. Results of the random-effects meta-analysis were presented using odds ratio (OR) estimates with 95% confidence intervals (CI). The Stata software version 16 was used for data analysis Results: In total, 18 studies with 514,577 subjects were suitable for reviewing maternal postpartum readmission by mode of delivery. Our findings show that cesarean delivery is a risk factor for increasing postpartum readmission than the vaginal delivery method (overall OR =1.42, CI 95% 1.22, 1.63, I2= 99.7%, p = 0.001). This chance was increased in primary cesarean (OR=2). Postpartum hemorrhage and breast infection did not have risk factors for postpartum readmission. While cardiopulmonary condition, deep vein thrombosis (DVT), genitourinary condition, and wound complication were risk factors among causes. Conclusion: Cesarean delivery is one of the most important risk factors for postpartum readmission, especially both childbirth-related/non-related complications causes. Therefore, it is necessary to revise the schedule of postpartum visits based on the type of delivery.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.041 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".