PCEA compared to continuous epidural infusion in an ultra‐low‐dose regimen for labor pain relief: a randomized study
Bibliographic record
Abstract
BACKGROUND: Patient-controlled epidural analgesia, PCEA, has been introduced in obstetric analgesia during the past decade. Many studies have shown that the consumption of analgesic is reduced when the parturient requests her own doses. This study investigates whether this is also true when using an ultra-low-dose regimen. METHODS: Eighty parturients were prospectively randomized to have either continuous epidural infusion (CEI) with ropivacaine 1 mg ml-1 and sufentanil 0.5 micro g ml-1, 6 ml h-1, or patient-controlled epidural analgesia (PCEA) with 4 ml demand doses with 20 min' lockout. The epidural start dose was the same for the two groups, 8 ml of the study solution. Rescue bolus doses were given when needed and the continuous infusion could be increased, which gave the two groups the same maximum possible dose. The consumption of local ropivacaine in combination with sufentanil during labor was registered. Hourly assessments made throughout labor included pain intensity documented with visual analog score, VAS, the patient's opinion on epidural efficacy, motor block, pruritus and need for nitrous oxide. RESULTS: The PCEA group consumed 33% less of the study solution than the CEI group. Mean total consumption was 35 ml (SD 18.0) and 52 ml (SD 19.6), respectively. Mean hourly consumption was 5.2 ml h-1 (SD 2.54) in the PCEA group and 6.9 ml h-1 (SD 1.31) in the CEI group. There were no significant differences between the two groups in pain relief, epidural efficacy, side-effects or obstetric outcome. CONCLUSION: PCEA reduces doses compared to continuous infusion even when ultra-low-dose local anesthetic with opioid is used. The PCEA technique provides individual titration of doses to an acceptable degree of pain relief.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".