Overlapping Compared With End-to-End Repair of Complete Third-Degree or Fourth-Degree Obstetric Tears
Bibliographic record
Abstract
OBJECTIVE: To report on a 3-year follow-up of women who underwent overlapping repair of a complete third-degree or fourth-degree obstetric tear. METHODS: Primiparous women sustaining a complete third-degree or a fourth-degree tear of the perineum were randomized to a primary sphincter repair using either an end-to-end or an overlapping surgical technique. At 1, 2, and 3 years, questionnaires on rates of flatal and fecal incontinence were mailed to participants. RESULTS: At 1 year, women who underwent an end-to-end repair reported lower rates of flatal and fecal incontinence than women who had an overlapping repair. For flatal incontinence the rates were 31% compared with 56% (95% confidence interval for the rate difference 6-43%, P=.012). For fecal incontinence, the rates were 7% compared with 16% (95% confidence interval for the rate difference -4% to 21%, P=.17). The difference between the two methods of surgical repair had largely disappeared by the end of year 2. CONCLUSION: At 1-year follow-up, end-to-end repair of complete third-degree or fourth-degree obstetric anal sphincter tears is associated with significantly lower rates of anal incontinence when compared with overlapping repair. There is no long-term benefit associated with either technique over the other. CLINICAL TRIAL REGISTRATION: ISRCTN Register, http://isrctn.org, ISRCTNO 4149919. LEVEL OF EVIDENCE: I.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".