Randomized Trial of Lidocaine Ointment Versus Placebo for the Treatment of Postpartum Perineal Pain
Bibliographic record
Abstract
In Brief OBJECTIVE To estimate the efficacy of lidocaine ointment in relieving pain after a vaginal delivery with an episiotomy or perineal laceration. METHODS In a randomized, double-blind, placebo-controlled trial, 200 women received lidocaine ointment (n = 108) or a placebo (n = 92). Pain relief was assessed by the amount of ointment used (weight of jar before use (weight of jar after use), total number of pain pills used, and a pain questionnaire. The sample size was calculated using a β of .2 and an β of .05 with an expected reduction of other pain medications from an average use of six pills to four pills for the population. RESULTS There was no significant difference in the amount of lidocaine versus placebo used for postpartum day 1 (5.1 g versus 4.0 g, respectively [P =.13]) or day 2 (3.7 g versus 2.6 g, respectively [P = 18]). Patients receiving lidocaine instead of the placebo showed no significant difference in the total amount of postpartum pain medications (6.3 versus 6.8 tablets, respectively [P = .53]), subjective pain parameters (P = .36), or satisfaction from ointment (P = .99). Patients with an episiotomy used more pain medications than those with a laceration (7.9 versus 5.6 tablets, respectively [P = .003]). Those with minor versus major lacerations required fewer pain pills (6.1 versus 10.8 tablets, respectively [P < .001]) and used less ointment (4.3 g versus 7.9 g, respectively [P = .02]) on the first postpartum day. CONCLUSION Topical application of 5% lidocaine ointment was not effective in relieving episiotomy or perineal laceration pain. Lidocaine ointment does not lessen perineal pain or decrease oral pain medications used after an episiotomy or perineal laceration.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".