Surgical Repair versus Non-Surgical Management of Spontaneous Perineal Tears that Occur during Childbirth
Bibliographic record
Abstract
Background: Trauma to the perineum of varying degrees constitutes the most common form of obstetric injury. In clinical practice, these tears are often sutured. However, small tears may also heal well without surgical interference. Aim: The aim of this study was to investigate whether surgical intervention for first and second-degree perineаl tears sustained during childbirth could аffect primary and secondary outcome compаred to conservative manаgement. Subject & Methods: Cohort Prospective study conducted in Obstetrics and Gynecological Department at Helwan General Hospital. One hundred women; 50 of them were using surgical repair by using suture for perineal tears compared with 50 ones leaving the wound to heal spontaneously, by using conservative management. Α Structured-Interviewing-Questionnaire-sheet, Physical-assessment-sheet, Labor-outcomes-sheet, McGill-pain-rating-scale, and follow-up sheet were used for data collection. Results: The majority (86.0 %) of women with surgical repair suffering from severe pain compared to 24.0% non-surgical repair group (PConclusion: There are evidence and significant differences between the two groups regarding type and intensity of pain. Moreover, there is evidence that the perineal tear did not heal so well in women up to six weeks postpartum who are not sutured. Recommendations: information sheets or booklets, that the mother can take home, should be distributed for postpartum women before their discharge to act as a reference for perineal tear and its proper care.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".