A new meta-analysis from Thomas Jefferson University in Philadelphia purports to have discovered a more accurate method of predicting the due dates of expecting mothers. The study, which involves measuring the length of the cervix, claims the technique will allow doctors to predict nearly exact due dates, according to CNN.

“Women always ask for a better sense of their delivery date in order to help them prepare for work leave, or to make contingency plans for sibling-care during labor,” said Dr. Vincenzo Berghella, director of maternal fetal medicine at Thomas Jefferson University Hospital. Dr. Berghella contends that improved due date prediction can help hospitals and expecting mothers plan for the birth.  

Typically, only 5% of women deliver on their estimated due dates. When, during the trials, the cervix measured was found to be 10 millimeters or less, there was an 85% chance that the baby would be within seven days. When it was less than 30 centimeters, it was found that there was a 50% chance.

There, however, critics who are less enthusiastic about the accuracy of the technique.

“This seems more like a party trick, rather than a useful tool,” said Dr. Katie Babaliaros, an OB-GYN with Peachtree Women’s Specialists in Atlanta.

Dr. Dian Tossy Fogle, a perinatologist at Northside Hospital Center for Perinatal Medicine in Atlanta, is also critical of the technique.

“Checking a cervical length at term only gives a window of time, in weeks, which a woman might deliver,” Fogle said.

Dr. Ashley Roman, director of the division of maternal fetal medicine at NYU Langone Medical Center, emphasizes the need for a more accurate due date prediction technique.

“The patient who has a partner who is traveling out of town or who is serving abroad in the military,” Roman said. “If the cervical length is less than 10 millimeters, it might be time to hang around.”

She remains skeptical of Berghella’s trials, however. “Even if the cervical length is greater than 30 millimeters, the chance of delivering in the next week is like flipping a coin. ‘Who knows?’ This data isn’t that helpful because this is what we already tell our patients.”