Nederlands
.png)
Total Bishop score: ... points
{{ model.survival.PITTitle }}
{{ model.survival.YNETitle }}
Total Bishop score: points
Outcome stratification
Conditional information
Result interpretation
How this model should be used:
Predicting which women will successfully deliver with induction can be difficult. The Bishop score is a standardized system used to evaluate the probability of successful vaginal delivery.
Context information:
The Bishop score for vaginal delivery and induction of pregnancy was originally developed to predict failure of elective induction in multiparous women. It is commonly considered for predicting the need for caesarean section after induction and preterm labor.
Note:
The scoring system was developed prior to cervical ripening agents.
References:
Bishop EH. Pelvic scoring for elective induction. Obstet Gynecol. 1964;24:266-8.
Calculations alone should never dictate patient care, and are no substitute for professional judgement. See our full disclaimer.
Model feedback
No feedback yet 1 Comment {{ model.comments.length }} CommentsValidation cohort size: 2916
C-Index: 0.66 |
Validation study authors: Newman RB, Goldenberg RL, Lams JD, Meis PJ, Mercer BM, Moawad AH, Thom E, Miodovnik E, Caritis SN, Dombrowski M, and Thurnau GR.
Validation author: Evidencio Community
Revision date: 2018-04-26
.png)
Please sign in to enable Evidencio print features
If you don't have an Evidencio Community Account you can create your free personal account at:
https://www.evidencio.com/registration
Printed results - Examples {{ new Date().toLocaleString() }}
Evidencio Community Account Benefits
With an Evidencio Community account you can:
- Create and publish your own prediction models.
- Share your prediction models with your colleagues, research group, organization or the world.
- Review and provide feedback on models that have been shared with you.
- Validate your models and validate models from other users.
- Find models based on Title, Keyword, Author, Institute, or MeSH classification.
- Use and save prediction models and their data.
- Use patient specific protocols and guidelines based on sequential models and decision trees.
- Stay up-to-date with new models in your field as they are published.
- Create your own lists of favorite models and topics.
{{ variable.title }}