Bibliographic record
Abstract
Patient-controlled epidural analgesia (PCEA) for labor was introduced into clinical practice 20 yr ago. The PCEA technique has been shown to have significant benefits when compared with continuous epidural infusion. We conducted a systematic review using MEDLINE and EMBASE (1988-April 1, 2008) of all randomized, controlled trials in parturients who received PCEA in labor in which one of the following comparisons were made: background infusion versus none; ropivacaine versus bupivacaine; high versus low concentrations of local anesthetics; and new strategies versus standard strategies. The outcomes of interest were maternal analgesia, satisfaction, motor block, and the incidence of unscheduled clinician interventions. A continuous background infusion improved maternal analgesia and reduced unscheduled clinician interventions. Larger bolus doses (more than 5 mL) may provide better analgesia compared with small boluses. Low concentrations of bupivacaine or ropivacaine provide excellent analgesia without significant motor block. Many strategies with PCEA can provide effective labor analgesia. High volume, dilute local anesthetic solutions with a continuous background infusion appear to be the most successful strategy. Research into new delivery strategies, such as mandatory programmed intermittent boluses and computerized feedback dosing, is ongoing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".