Labor neuraxial analgesia and breastfeeding: An updated systematic review
Bibliographic record
Abstract
INTRODUCTION: There have been numerous reports studying the effect of neuraxial analgesia on breastfeeding success, but the results are inconsistent. METHODS: We performed a literature search in various databases for studies comparing neuraxial analgesia to non-neuraxial or no analgesia. Outcomes were the percentage of women breastfeeding fully or mixed with formula. Where possible, nulliparous parturients were analyzed separately. We conducted an analysis excluding studies of serious and critical risk of bias. Odds ratios and 95% confidence intervals were calculated. RESULTS: We included 15 studies (13 observational studies, 1 secondary analysis of a randomized controlled trial, 1 case-control study) with 16,112 participants. Overall, there were 6 studies that found no difference between groups, 6 studies that showed a significantly lower incidence of breastfeeding in the neuraxial group and 3 studies finding mixed results (at some time-points statistically significant and at some time-point statistically non-significant results). In nulliparous only studies, 2 found no difference between study groups, 1 found a lower breastfeeding rate in the neuraxial group and 3 studies showed mixed results. Excluding studies with a serious and critical risk of bias, 1 study found no difference between study groups, 3 studies found a decrease of breastfeeding rates in the neuraxial group, and 1 study showed mixed results. DISCUSSION: In our review we found a high disparity in results. One reason is probably the high potential of confounding (immediate skin to skin placement, maternity leave etc.). Education programs and breastfeeding support are likely more important in determining long term breastfeeding success.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".