Methods for assessing pre-induction cervical ripening

  • Protocol
  • Intervention


  • Ifeanyichukwu U Ezebialu,

    Corresponding author
    1. Faculty of Clinical medicine, College of Medicine, Anambra State University Amaku,, Department of Obstetrics and Gynaecology, Awka, Nigeria
    • Ifeanyichukwu U Ezebialu, Department of Obstetrics and Gynaecology, Faculty of Clinical medicine, College of Medicine, Anambra State University Amaku,, Awka, Nigeria.

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  • Ahizechukwu C Eke,

    1. Michigan State University School of Medicine/Sparrow Hospital, Department of Obstetrics and Gynecology, Lansing, Michigan, USA
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  • George U Eleje,

    1. Nnamdi Azikiwe University Teaching Hospital, Department of Obstetrics and Gynaecology, Nnewi, Anambra State, Nigeria
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  • Chukwuemeka E Nwachukwu

    1. Excellence & Friends Management Consult (EFMC), Abuja, FCT, Nigeria
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This is a protocol for a Cochrane Review (Intervention). The objectives are as follows:

To compare different methods of pre-induction cervical assessment for women admitted for induction of labour.


Induction of labour is the artificial initiation of labour in a pregnant woman after the age of fetal viability but without any objective evidence of active phase labour and with intact fetal membranes. This procedure is increasingly being carried out in obstetric units for varying indications (Baacke 2006; Crane 2006). The need for induction of labour may arise due to a problem in the mother, her fetus or both, and the procedure may be carried out at or before term. Obstetricians have long known that for this to be successful, it is important that the uterine cervix (the neck of the womb) has favourable characteristics in terms of readiness to go into the labour state (Baacke 2006; Edwards 2000). The definition of failed induction of labour has controversies surrounding it but the risks are clear. Because of the risks of failed induction of labour, a variety of maternal and fetal factors as well as screening tests have been suggested to predict labour induction success (Crane 2006). These include certain maternal factors such as parity (the number of times a woman has delivered), height, weight, body mass index, maternal age, Bishop score and its individual components, fetal factors such as birthweight and gestational age, transvaginal ultrasound (TVUS) assessment of the cervix, and biochemical markers including fetal fibronectin (fFN) and insulin-like growth factor binding protein-1 (IGFBP-1).

Description of the condition

During pregnancy, the cervix is a solid and closed organ. As the pregnancy advances towards the time of labour, the cervix undergoes some stages of remodelling in readiness for delivery (Timmons 2010). The first phase of the remodelling is the stage of softening which involves a decline in the tissue tensile strength and this may start in the first trimester (the first 13 weeks of pregnancy). This first stage is usually slow but progressive and requires the progesterone-rich environment to take place. In the weeks preceding spontaneous labour and delivery, the next stage of cervical ripening commences (Timmons 2010; Word 2007). It is only after the cervix has ripened that it can dilate in response to spontaneous uterine contractions. The process of cervical ripening is a complex one that is associated with an increase in the concentration of the hydrophilic (water attracting) glycosaminoglycans and non collagenous proteins (Leppert 1995; Word 2007). This phase of cervical remodelling is therefore very important as this is what gives the cervix the ability to dilate (open up) in response to uterine contractions of labour. It is not very clear what the role of chemicals such as prostaglandins is in the natural ripening process but it is known that administration of prostaglandins or their analogues will lead to ripening of the cervix in a woman with an unripe cervix. Some studies have also shown that the histological features of a naturally ripened cervix are similar to that induced by exogenous prostaglandins (Rath 1993; Uldbjerg 1983; Word 2007). Although induction of labour is an artificial procedure, it tries to, as much as possible, to mimic the physiological process. Therefore, to expect a successful induction of labour, care should be taken to determine if the cervix is ripe. For the unripe cervix, certain agents should be used to ripen the cervix in order to optimise the success of labour induction.

Description of the intervention

Several methods have been used to assess the ripeness of the cervix prior to labour induction and newer methods are being sought. The traditional method is the cervical scoring system described by Bishop, known as the Bishop score (Bishop 1964). This system assesses the position, consistency, effacement, dilatation of the maternal cervix, as well as the station of the fetal presenting part. The maximum score here is 13 and studies have shown that women with a score of nine or more were more likely to have successful labour induction (Baacke 2006). In his modification, Burnett discovered that women with a score of at least six achieved vaginal birth within six hours in 90% of the time, whereas, the course of labour was unpredictable in women with a score less than six (Baacke 2006; Burnett 1966). Some other studies have also considered a score of six or more as favourable for labour induction (Eggebo 2009). Although Bishop described his method to predict the success of labour induction in parous women with cephalic presentation, the system is used today for every proposed induction of labour (Baacke 2006). This original scoring system is simple to perform but several questions have arisen concerning its ability to objectively assess cervical ripening prior to labour induction and so several modifications of the system have been proposed (Baacke 2006; Burnett 1966; Eggebo 2009; Goldberg 1997; Keepanasseril 2012). Some studies have even shown that it is a poor predictor of the outcome of labour (Hendrix 1998). This has led researchers into searching for alternative methods that may be more objective.

Transvaginal ultrasound (TVUS) assessment of the cervix was subsequently introduced to assess pre-induction cervical ripening (Keepanasseril 2007; Pandis 2001; Rane 2004; Rane 2005; Yang 2004). TVUS is able to measure objectively the cervical length, internal os diameter and the posterior angle. This method has been used to predict preterm delivery (Goldberg 1997) and has found a place in the assessment of pre-induction cervical ripening (Crane 2006). Studies have compared the performance of the Bishop score and TVUS cervical assessment in the prediction of the outcome of labour induction and have given mixed results (Crane 2006; Eggebo 2009; Yang 2004). In some studies, TVUS cervical assessment proved superior to the Bishop score (Pandis 2001; Rane 2004), while in others the superiority was not demonstrated (Gonen 1998; Rozenberg 2000; Rozenberg 2005).

Some chemicals related to pregnancy have also been studied for predicting the success of labour induction. Fetal fibronectin is a glycoprotein that is found in the amniotic fluid and choriodecidual interface in high concentrations but leaks into the vaginal secretion prior to the onset of spontaneous labour (Baacke 2006; Mouw 1998; Roman 2004). In his study, Ahner et al noted that women who tested positive to fFN in their vaginal secretion were more likely to deliver within 24 hours (Ahner 1995). The same study also showed that women with both a low Bishop score and negative fFN test had the highest risk of prolonged labour and operative deliveries. Another study has compared the Bishop score and fFN testing and concluded that their performances were similar (Blanch 1996). Ekman et al also documented that a positive vaginal fFN correlated well with cervical ripeness but recommended a quantitative study to determine the threshold value that can be a cut-off point for ripeness (Ekman 1995). Another chemical that has been evaluated is Insulin-like growth factor binding protein-1 (IGFBP-1). It exists in different parts of the body as isoforms depending on its phosphorylation status. The amniotic fluid contains mainly the non phosphorylated isoform while the decidual tissues contain the phosphorylated isoform (Martina 1997; Westwood 1994). Nuutila and his group have evaluated IGFBP-1 to see if its presence in cervical secretion reflects the ripeness of the cervix (Nuutila 1999).

How the intervention might work

The physiologic cervical ripening that predates spontaneous uterine contractions is associated with shortening of the cervix (effacement), opening of the internal cervical os (dilatation) and softening of the cervix (consistency). To be able to assign a Bishop score, a vaginal examination is performed to assess the state of the cervix in terms of its consistency, dilatation, position and effacement as well as the station of the fetal presenting part. Scores are assigned to each parameter and the total score becomes the Bishop score. The higher the score, the more the cervix is ripe and therefore, successful induction of labour is expected. Harrison et al did show that up to 87% of women with a Bishop score of at least seven will deliver within nine hours, whereas only 44% of those with score of four or less will deliver within the same time frame (Harrison 1977). It may be difficult to fully assess the cervical length especially when the internal os is closed as the finger may not reach the part of the cervix beyond the vaginal fornices (Crane 2006). TVUS is able to measure the cervical length and cervical funnelling (representing dilatation), which are changes associated with cervical ripening (Baacke 2006). TVUS is also able to measure the posterior cervical angle and studies have shown that a value of more than 90 degrees predicts successful vaginal birth (Eggebo 2009; Rane 2004). In another study, cervical length assessment by TVUS predicted successful induction of labour (Yang 2004).

Fetal fibronectin exists in the choriodecidual space and the amniotic fluid and its production increases with advancing gestational age. Uterine activity leads to its leakage into the cervico-vaginal secretion even with an intact fetal membrane (Mouw 1998; Ojutiku 2002). It has been suggested that this leakage precedes the onset of labour by about two weeks and may represent the later stages of physiological events before the onset of labour (Garite 1996). Therefore, its detection in the cervico-vaginal secretion may suggest imminent labour. Ahner et al did show that women with intact fetal membrane undergoing induction of labour were more likely to deliver within 24 hours if they had positive fFN (Ahner 1995). IGFBP-1 exists as the phosphorylated isoform in the choridecidual space and leaks into the cervix and vagina with increasing choriodecidual activity (Martina 1997; Westwood 1994). Its role in the prediction of preterm birth has been assessed by different authors. One study assessed it among symptomatic women and documented a positive predictive and negative predictive values of 24% and 86% for delivery before 37 weeks (Cooper 2012). in another study, the positive and negative predictive value for preterm delivery before 35 weeks were 47% and 93% respectively (Elizur 2005).

Why it is important to do this review

Obstetricians, over the years, have known that the pre-induction cervical status is an important determinant of the outcome of induction of labour. Despite the different modifications, the Bishop score remains the most popular way of assessing the cervix for ripeness but its objectivity and ability to predict vaginal delivery have been contested (Baacke 2006). Currently, there is no strong evidence to suggest the most dependable method for assessing pre-induction cervical ripening since different studies give inconsistent findings (Gonen 1998; Pandis 2001). If such evidence becomes available, clinicians will be guided appropriately in order to optimise the outcome of labour induction.


To compare different methods of pre-induction cervical assessment for women admitted for induction of labour.


Criteria for considering studies for this review

Types of studies

The review will include all randomised control trials that compare Bishop score with any other methods of pre-induction cervical assessment in women admitted for induction of labour. Cluster-randomised studies will also be included but quasi-randomised studies will not be included. Where abstracts contain sufficient information to allow data to be extracted then this will be done, otherwise authors will be contacted for additional information.

Types of participants

Women who were admitted for induction of labour and are carrying singleton pregnancies in cephalic presentation at a gestational age of at least 24 completed weeks.

Types of interventions

Trials comparing Bishop score with any one or more other methods of assessing pre-induction cervical ripening (e.g. transvaginal ultrasonography (TVUS), insulin-like growth factor binding protein-1 (IGFBP-1) and vaginal fetal fibronectin).

  • Bishop score versus TVUS.

  • Bishop score versus IGFBP-1.

  • Bishop score versus vaginal fetal fibronectin.

Types of outcome measures

Primary outcomes
  1. Induction-delivery interval.

  2. Vaginal birth.

  3. Caesarean delivery.

  4. Neonatal admission into neonatal intensive care unit.

  5. Perinatal mortality.

Secondary outcomes
  1. Interval from induction to active phase labour.

  2. Use of misoprostol or other ripening agents.

  3. Meconium staining of the amniotic fluid.

  4. Fetal heart rate abnormality.

  5. Apgar score less than seven.

  6. Uterine rupture.

Search methods for identification of studies

Electronic searches

We will search the Cochrane Pregnancy and Childbirth Group’s Trials Register by contacting the Trials Search Co-ordinator. 

The Cochrane Pregnancy and Childbirth Group’s Trials Register is maintained by the Trials Search Co-ordinator and contains trials identified from: 

  1. monthly searches of the Cochrane Central Register of Controlled Trials (CENTRAL);

  2. weekly searches of MEDLINE;

  3. weekly searches of Embase;

  4. handsearches of 30 journals and the proceedings of major conferences;

  5. weekly current awareness alerts for a further 44 journals plus monthly BioMed Central email alerts.

Details of the search strategies for CENTRAL, MEDLINE and Embase, the list of handsearched journals and conference proceedings, and the list of journals reviewed via the current awareness service can be found in the ‘Specialized Register’ section within the editorial information about the Cochrane Pregnancy and Childbirth Group

Trials identified through the searching activities described above are each assigned to a review topic (or topics). The Trials Search Co-ordinator searches the register for each review using the topic list rather than keywords.  

Searching other resources

We will also review citations and references from identified studies for relevant publications as well as contact experts, trialists and authors for unpublished and ongoing trials.

We will not apply any language restrictions. 

Data collection and analysis

Data collection and analysis will be conducted in accordance with the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011).

Selection of studies

Two review authors will independently assess the eligibility and methodological quality of all the potential studies we identify as a result of the search strategy, without consideration of trial results. We will resolve any disagreement through discussion or, if required, consult a third author.

Data extraction and management

Two review authors will independently apply the inclusion criteria to potentially relevant trials with a pre-designed and validated data extraction form. For eligible studies, two review authors will extract the data using the agreed form.We will resolve discrepancies through discussion or, if required, we will consult a third author. We will enter data into Review Manager software (RevMan 2011) and check for accuracy. When information regarding any of the above is unclear, we will attempt to contact authors of the original reports to provide further details.

Assessment of risk of bias in included studies

Two review authors will independently assess risk of bias for each study using the criteria outlined in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011). We will resolve any disagreement by discussion or by involving a third assessor.

For cluster-randomised studies, special methods will be used to assess for the risk of bias as outlined in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011).

(1) Random sequence generation (checking for possible selection bias)

For each included study, we will describe the method used to generate the allocation sequence in sufficient detail to allow an assessment of whether it should produce comparable groups.

We will assess the method as:

  • low risk of bias (any truly random process, e.g. random number table; computer random number generator);

  • high risk of bias (any non-random process, e.g. odd or even date of birth; hospital or clinic record number);

  • unclear risk of bias.

(2) Allocation concealment (checking for possible selection bias)

For each included study, we will describe the method used to conceal allocation to interventions prior to assignment and will assess whether intervention allocation could have been foreseen in advance of, or during recruitment, or changed after assignment.

We will assess the methods as:

  • low risk of bias (e.g. telephone or central randomisation; consecutively numbered sealed opaque envelopes);

  • high risk of bias (open random allocation; unsealed or non-opaque envelopes, alternation; date of birth);

  • unclear risk of bias.   

(3.1) Blinding of participants and personnel (checking for possible performance bias)

For each included study, we will describe the methods used, if any, to blind study participants and personnel from knowledge of which intervention a participant received. We will consider that studies are at low risk of bias if they were blinded, or if we judge that the lack of blinding would be unlikely to affect results. We will assess blinding separately for different outcomes or classes of outcomes.

We will assess the methods as:

  • low, high or unclear risk of bias for participants;

  • low, high or unclear risk of bias for personnel.

Where needed “partial” can be added to the list of options for assessing quality of blinding.

(3.2) Blinding of outcome assessment (checking for possible detection bias)

For each included study, we will describe the methods used, if any, to blind outcome assessors from knowledge of which intervention a participant received. We will assess blinding separately for different outcomes or classes of outcomes.

We will assess methods used to blind outcome assessment as:

  • low, high or unclear risk of bias.

(4) Incomplete outcome data (checking for possible attrition bias due to the amount, nature and handling of incomplete outcome data)

For each included study, we will describe and for each outcome or class of outcomes, the completeness of data including attrition and exclusions from the analysis. We will state whether attrition and exclusions were reported and the numbers included in the analysis at each stage (compared with the total randomised participants), reasons for attrition or exclusion where reported, and whether missing data were balanced across groups or were related to outcomes.  Where sufficient information is reported, or can be supplied by the trial authors, we will re-include missing data in the analyses which we undertake.

We will assess methods as:

  • low risk of bias (e.g. no missing outcome data; missing outcome data balanced across groups);

  • high risk of bias (e.g. numbers or reasons for missing data imbalanced across groups; ‘as treated’ analysis done with substantial departure of intervention received from that assigned at randomisation);

  • unclear risk of bias.

(5) Selective reporting (checking for reporting bias)

For each included study, we will describe how we investigated the possibility of selective outcome reporting bias and what we found.

We will assess the methods as:

  • low risk of bias (where it is clear that all of the study’s pre-specified outcomes and all expected outcomes of interest to the review have been reported);

  • high risk of bias (where not all the study’s pre-specified outcomes have been reported; one or more reported primary outcomes were not pre-specified; outcomes of interest are reported incompletely and so cannot be used; study fails to include results of a key outcome that would have been expected to have been reported);

  • unclear risk of bias.

(6) Other bias (checking for bias due to problems not covered by (1) to (5) above)

For each included study, we will describe any important concerns we have about other possible sources of bias.

We will assess whether each study was free of other problems that could put it at risk of bias:

  • low risk of other bias;

  • high risk of other bias;

  • unclear whether there is risk of other bias.

(7) Overall risk of bias

We will make explicit judgements about whether studies are at high risk of bias, according to the criteria given in the Handbook (Higgins 2011). With reference to (1) to (6) above, we will assess the likely magnitude and direction of the bias and whether we consider it is likely to impact on the findings. We will explore the impact of the level of bias through undertaking sensitivity analyses - see Sensitivity analysis

Measures of treatment effect

Dichotomous data

For dichotomous data, we will present results as summary risk ratio with 95% confidence intervals. 

Continuous data

For continuous data, we will use the mean difference if outcomes are measured in the same way between trials. We will use the standardised mean difference to combine trials that measure the same outcome, but use different methods.

Unit of analysis issues

We will include cluster-randomised trials in the analyses along with individually-randomised trials. We will adjust their sample sizes using the methods described in the Handbook using an estimate of the intracluster correlation co-efficient (ICC) derived from the trial (if reported), from a similar trial or from a study of a similar population. If we use ICCs from other sources, we will report this and conduct sensitivity analyses to investigate the effect of variation in the ICC. The actual sample sizes and number of those experiencing the event of such cluster-randomised trials will be determined by dividing them by the design effect (DE) derives as: DE = 1+(M-1) ICC where M is the average cluster size and ICC is the intracluster correlation coefficient.

We will investigate the influence of the unit of randomisation (individual versus cluster) on effect estimates by undertaking a sensitivity analysis.

If an included study has multiple intervention arms and all the arms are relevant to the review, we will combine all the comparison intervention groups of the study into a single group. However, if some of the arms are not relevant to the review, we will select one pair of interventions and exclude the others.

Dealing with missing data

For included studies, we will note levels of attrition. We will explore the impact of including studies with high levels of missing data in the overall assessment of treatment effect by using sensitivity analysis.

For all outcomes, we will carry out analyses, as far as possible, on an intention-to-treat basis, i.e. we will attempt to include all participants randomised to each group in the analyses, and all participants will be analysed in the group to which they were allocated, regardless of whether or not they received the allocated intervention. The denominator for each outcome in each trial will be the number randomised minus any participants whose outcomes are known to be missing.

Assessment of heterogeneity

We will assess statistical heterogeneity in each meta-analysis using the T², I² and Chi² statistics. We will regard heterogeneity as substantial if an I² is greater than 30% and either the T² is greater than zero, or there is a low P value (less than 0.10) in the Chi² test for heterogeneity. 

Assessment of reporting biases

If there are 10 or more studies in the meta-analysis we will investigate reporting biases (such as publication bias) using funnel plots. We will assess funnel plot asymmetry visually. If asymmetry is suggested by a visual assessment, we will perform exploratory analyses to investigate it.

Data synthesis

We will carry out statistical analysis using the Review Manager software (RevMan 2011). We will use fixed-effect meta-analysis for combining data where it is reasonable to assume that studies are estimating the same underlying treatment effect: i.e. where trials are examining the same intervention, and the trials’ populations and methods are judged sufficiently similar. If there is clinical heterogeneity sufficient to expect that the underlying treatment effects differ between trials, or if substantial statistical heterogeneity is detected, we will use random-effects meta-analysis to produce an overall summary, if an average treatment effect across trials is considered clinically meaningful. The random-effects summary will be treated as the average range of possible treatment effects and we will discuss the clinical implications of treatment effects differing between trials. If the average treatment effect is not clinically meaningful, we will not combine trials.

If we use random-effects analyses, the results will be presented as the average treatment effect with 95% confidence intervals, and the estimates of  T² and I².

Subgroup analysis and investigation of heterogeneity

If we identify substantial heterogeneity, we will investigate it using subgroup analyses and sensitivity analyses. We will consider whether an overall summary is meaningful, and if it is, use random-effects analysis to produce it.

We plan to carry out the following subgroup analyses.

  1. Women who have not delivered any baby previously (nulliparous women) versus those who have delivered previously.

  2. Induction of labour carried out at a gestational age less than 37 completed weeks versus those conducted at 37 completed weeks or more (i.e. preterm versus term).

  3. Induction of labour for a live fetus versus induction for intrauterine fetal death.

Subgroup analysis will be restricted to the primary outcomes.

We will assess subgroup differences by interaction tests available within RevMan (RevMan 2011). We will report the results of subgroup analyses quoting the χ2 statistic and P value, and the interaction test I² value.

Sensitivity analysis

We will carry out sensitivity analysis to explore the effects of trial quality assessed by allocation concealment and other risk of bias components, by omitting studies rated as ’high risk of bias’ for these components. We will restrict this to the primary outcomes We will compare meta-analyses of trials in which the unit of randomisation was at the individual level compared with those at the cluster level.


We acknowledge the Nigerian branch of the South African Cochrane Centre for training us on the methods for writing systematic review.

As part of the pre-publication editorial process, this protocol has been commented on by three peers (an editor and two referees who are external to the editorial team) and the Group's Statistical Adviser.

The National Institute for Health Research (NIHR) is the largest single funder of the Cochrane Pregnancy and Childbirth Group. The views and opinions expressed therein are those of the authors and do not necessarily reflect those of the NIHR, NHS or the Department of Health.

Contributions of authors

Ifeanyichukwu Ezebialu conceived, designed and co-ordinated the protocol. Ahizechukwu Eke, George Eleje and Chukwuemeka Nwachukwu carried out the literature search that informed the Background and also contributed to writing the protocol. All review authors read and approved the final version.

Declarations of interest

None known.