A Retrospective Study of Maternal and Neonatal Outcomes Following Conventional and Water Birth in Ecuador
Bibliographic record
Abstract
OBJECTIVES: Demonstrate maternal and neonatal complications reported in women giving birth in water birth compared to those conventional land births. METHODS: An observational retrospective analysis of the incidence of maternal and neonatal outcomes among 358 women who deliver their newborns throughout conventional vaginal delivery and 308 women giving birth in water during 2013 in Quito, Ecuador. Maternal Age, Educational attainment, Neonatal weight, height, APGAR scores, vaginal tearing and the need to resuscitate a newborn were the variables matched for both groups. RESULTS: Among 308 women who were scheduled to deliver their newborns in water, 73% successfully culminate in water vaginal births while 26 % required a C-section. Among the conventional birth group, only 47% ended their pregnancy vaginally as planned and 53% required a C-section. The use of oxytocin (RR: 12.9 CI 7.9 to 20.9 p<0.0001) and intentional episiotomy (RR: 13.9 CI 5.1 to 37.9 p<0.0001) are much higher among conventional birth, however, the risk to have a vaginal tearing during water labor is 3 times higher than conventional birth (RR: 2.9 CI 2.12 to 4.2 p<0.0001). In the conventional delivery cohort 3 neonatal deaths were reported while water birth no deaths reported, however, no causality of these deaths was explored due to the absence of information. CONCLUSIONS: We conclude that water birth is an effective method to deliver children as long as there is an adequate understanding of the risk and benefits of this procedure. Planning a water delivery seems to reduce the risk of using prophylactic uterotonic medication, prophylactic episiotomies and to perform unplanned C-sections. Due to higher incidence of vaginal tears, strict perianal protection during the third stage of labor is recommended.
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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 | 0.003 |
| 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".