Randomized Trial Comparing Three Methods of Perineal Skin Closure at the Time of Second Degree Repair [12E]
Bibliographic record
Abstract
INTRODUCTION: We aimed to test the null hypothesis that there is no difference in patient pain among three different methods of perineal skin closure during second-degree repair: 1) suture, 2) no suture, and 3) surgical glue. METHODS: A single-blind randomized controlled trial of women post vaginal birth with a second-degree perineal laceration was conducted at a tertiary care teaching hospital August 2014-April 2017.Following repair of the deep tissues per standard fashion, women were randomized to perineal skin closure with suture, no suture or surgical glue using a 1:1:1 allocation. Pain was assessed using the McGill, 100-mm VAS and Present Pain Index at 1 day, 2 weeks and 6 weeks postpartum. RESULTS: 35 women were randomized: 14 received suture, 11 no suture and 10 had surgical glue. Demographics were similar between groups. At 2 weeks, women with suture had higher pain scores than those with surgical glue or no suture (McGill: suture vs glue vs no suture, 16.2 ± 9.9 vs 6.4 ± 8.4 vs 4.7 ± 5.8, ANOVA p = .021; VAS: 50.6 ± 31.7 vs 17.7 ± 25.7 vs 10.9 ± 12.2, ANOVA p=.009). At 6 weeks, pain scores remained higher with suture versus glue (McGill, p = .045) and no suture (VAS, p = .047). No difference in pain was seen between women with glue vs no suture. CONCLUSION: Compared to no suture and surgical glue, suturing the perineal skin was associated with the highest pain scores 6 postpartum weeks. Pain with surgical glue and no suture was similar.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".