Rectal analgesia for the relief of perineal pain after childbirth: a randomised controlled trial of diclofenac suppositories
Bibliographic record
Abstract
OBJECTIVE: To evaluate rectal diclofenac in the relief of perineal pain after trauma during childbirth. DESIGN: A randomised, double-blind trial. SETTING: Delivery Suite, Women's and Children's Hospital, South Australia. POPULATION: Women with a second-degree (or greater) perineal tear or episiotomy. METHODS: Women were randomly allocated to either diclofenac or placebo suppositories (Anusol), using a computer-generated randomisation schedule with stratification for parity and mode of birth. Treatment packs contained two x 100 mg diclofenac or two placebo suppositories, the first being inserted when suturing was complete, and the second 12-24 hours after birth. Women were asked to complete questionnaires at 24 and 48 hours after birth relating to their degree of perineal pain using the validated Short Form McGill Pain Questionnaire. MAIN OUTCOME MEASURES: Pain scores at 24 and 48 hours after birth. RESULTS: A total of 133 women were recruited, with 67 randomised to diclofenac suppositories and 66 to placebo. Women in the diclofenac group were significantly less likely to experience pain at 24 hours while walking (RR 0.8; 95% CI 0.6 to 1.0), sitting (RR 0.8; 95% CI 0.6 to 1.0), passing urine (RR 0.6; 95% CI 0.4 to 1.0) and on opening their bowels (RR 0.6; 95% CI 0.2 to 0.9) compared with those women who received placebo. These differences were not sustained 48 hours after birth. CONCLUSIONS: The use of rectal non-steroidal anti-inflammatory drug suppositories is a simple, effective and safe method of reducing the pain experienced by women following perineal trauma within the first 24 hours after childbirth.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| 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 | 0.002 |
| 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".