Looking Beyond the Pain: Can Effective Labor Analgesia Prevent the Development of Postpartum Depression?
Bibliographic record
Abstract
(Anesth Analg. 2018;126:1448–1450) There is emerging evidence of an association among acute postpartum pain, chronic pain, and postpartum depression (PPD). Authors of this editorial discuss the relationship of pain during labor and delivery to PPD found in recent studies. In 2014, Ding et al found the use of labor epidural analgesia to be associated with a reduced risk of PPD [odds ratio, 0.3; 95% confidence interval (CI), 0.12-0.79]. However, this observational study was limited by potential misclassification bias and uncontrolled confounding. A 2018 study by Orbach-Zinger et al found that the rate of PPD among women who intended to use epidural analgesia but delivered without was not different compared with all other groups. In secondary analysis, they found that the relative risk of PPD was, in fact, higher among women who ultimately received epidural analgesia than those who did not use epidural analgesia (risk difference, 7.2%; 95% CI, 2.3%-12.1%). The authors acknowledged that unplanned epidural analgesia may be an indicator for physiologically difficult delivery. Lim et al examined the relationship between quality of intrapartum analgesia and PPD, finding an inverse association between greater improvements in pain and Edinburgh Postnatal Depression Scale scores. Although the effect of labor analgesia on the scores was found to be relatively small, worse labor analgesia was significantly associated with positive score screen (adjusted odds ratio, 6.6; 95% CI, 1.9-22.4).
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".