Induction of Labor and Risk of Third- and Fourth-Degree Perineal Tears [18P]
Bibliographic record
Abstract
INTRODUCTION: The rate of induction of labor (IOL) at term has been increasing over the years. Our objective was to evaluate the risk of third- and fourth-degree perineal tears associated with term induction of labor. METHODS: We conducted a population-based, retrospective cohort study using the United States' Nationwide Inpatient Sample to evaluate the risk of third- and fourth-degree perineal tears in women who underwent IOL at term between 2005 and 2014. We included all term livebirths and excluded, preterm births, previous cesarean deliveries, multifetal gestations, and non-cephalic presentation. Women who underwent an IOL at term were identified using ICD-9 coding. Patient characteristics were compared between those women being induced at term with those who weren't, and logistic regression analysis were carried out to estimate the adjusted effect of IOL at term with risk of 3rd and 4th perineal tears RESULTS: Among 5,982,945 eligible live births, 1,035,003 (17.3%) underwent an IOL at term, increasing from 15.7% to 18.5%. Women with an IOL were more likely to be older, Caucasian, and with comorbid illnesses. Compared with women who did not undergo an IOL, women in the IOL group had lower risk of cesarean deliveries, 0.89 (0.88–0.89), 2nd degree tears, 0.89 (0.89–0.90), 3rd degree tears, OR 0.81 (95% CI 0.79–0.82) and 4th degree tears, OR 0.84 (95% CI 0.82–0.87). CONCLUSION: Induction of labor at term results in significantly lower risk of third- and fourth-degree perineal tears among all deliveries as well as among women with vaginal deliveries.
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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 | 0.000 | 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 | 0.000 |
| 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".