Risk Factors for Anal Sphincter Tear in Multiparas
Bibliographic record
Abstract
OBJECTIVE: To assess maternal, newborn, and obstetric risk factors associated with anal sphincter tear in multiparous women. METHODS: This case-control study identified 18,779 multiparous vaginal deliveries from 1992 to 2004 from an obstetric automated record database at the University of Alabama at Birmingham. Two hundred eighty-four patients were selected, 145 cases and 139 controls. Variables from the index pregnancy and prior pregnancies were analyzed, and multivariable logistic regression models were constructed to determine significant predictor variables for anal sphincter tear in multiparous women. RESULTS: One hundred forty-five multiparous women with no history of cesarean delivery sustained a sphincter tear. Multivariable logistic regression showed a significant association with episiotomy (odds ratio [OR] 16.3, 95% confidence interval [CI] 7.7-34.4), shoulder dystocia (OR 7.9, CI 1.6-38), forceps delivery (OR 4.7, CI 2.0-11.2), and being married (OR 2.2, CI 1.1-4.6). A second exploratory model that included variables from previous pregnancies, showed that in addition to episiotomy (OR 34.6, CI 8.8-136), shoulder dystocia (OR 11.1, CI 1.3-95.2), forceps delivery (OR 6.1, CI 1.6-23.5), previous sphincter tear (OR 7.7, CI 1.2-48.7), and second stage of labor greater than 1 hour (OR 6.7, CI 1.1-42.5) were associated with tear. CONCLUSION: The strongest clinical risk factors for anal sphincter tear in multiparous women are episiotomy, shoulder dystocia, previous sphincter tear, prolonged second stage of labor, and forceps delivery. LEVEL OF EVIDENCE: II-2.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".