Using Adhesive Glue to Repair First Degree Perineal Tears: A Prospective Randomized Controlled Trial
Bibliographic record
Abstract
Our objective was to evaluate the effectiveness of adhesive glue in repairing first degree perineal tears. We conducted a noninferiority prospective, randomized, controlled trial comparing adhesive glue with traditional suturing. Each case was evaluated immediately after birth and after the puerperium. The two-sample t-test and the Mann-Whitney nonparametric test were applied to compare quantitative variables between the treatment groups. The chi-squared test and Fisher's exact test were used to assess the association between qualitative variables. A total of 102 women participated, 28 in the suture arm and 74 in the adhesive glue arm. While cosmetic and functional results of adhesive glue use were not inferior to suturing, the use of adhesive glue was associated with a shorter procedure, less need for local anesthetic, less pain, and greater satisfaction. Our results suggest a novel approach for the repair of common postpartum first degree lacerations. The use of adhesive glue achieves cosmetic and functional results equal to traditional suturing and offers some immediate advantages for the patient. While further clinical trials are needed to validate our results, it is important to inform obstetrician of the possible use of adhesive glue in these very common clinical scenarios. This trial is registered with NCT00746707.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".