Quality of labor epidural analgesia at a high-volume tertiary care obstetric unit: a before-and-after study
Bibliographic record
Abstract
INTRODUCTION: We wanted to better understand the quality of our labor epidural practice at a large urban academic medical center. Several practice changes were implemented between 2011 and 2017, namely a more uniform epidural loading dose of local anesthetic that includes fentanyl, an increase in both the hourly baseline offer and maximum allowed hourly amount of bupivacaine, and the change from a continuous epidural infusion to a programmed intermittent epidural bolus (PIEB) regimen. We aimed to assess the impact of those changes on the quality of labor analgesia. METHODS: We performed two separate audits representing before-and-after groups. The audits were performed in November 2011 (before group) and November-December 2017 (after group). The data for 2011 were extracted from a previously published study. Hence, we conducted a similar audit in 2017, including only outcomes that were included in the previous audit. The primary outcome was the presence of pain >3 (Numerical Rating Scale 0-10) at any time during first or second stage of labor. Secondary outcomes included top-up requirements, and women's pain perception during the first and second stage of labor according to a postpartum questionnaire. RESULTS: We studied 294 and 247 women in the before-and-after groups, respectively. The proportion of women reporting pain >3/10 at any time during labor and delivery significantly decreased in the after group (30% vs 41%; p<0.01). In an adjusted analysis, there was a 35% reduction in the likelihood of pain scores>3 for the after group (OR 0.65, 95% CI 0.46, 0.94). Women in the after group received fewer top-ups by nurses (3% vs 24%, p<0.001). Most women in both cohorts (85% before and 87% after) were satisfied with the overall quality of analgesia. DISCUSSION: A bundle of practice changes implemented in our clinical practice, including the PIEB regimen, has resulted in a significant improvement in the quality of labor analgesia. However, despite all the implemented changes, 30% of women still experience pain during labor and further optimization of our practice is warranted.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".