Uterine rupture is often viewed as a catastrophic event, often occurring in women who have undergone a cesarean section for re-birth. Previous studies have often focused attention on predicting uterine rupture and maternal and infant risk. Recently, RiddellCA et al. of the Department of Obstetrics and Gynecology at Columbia University in Canada examined whether the occurrence of rupture affects obstetric cesarean delivery and labor management and published in the journal Obstetrics & Gynecology. To determine the extent to which vaginal birth after cesarean section (VBAC) rates, trial of vaginal delivery after cesarean section (TOLAC) rates, or trial of labor success rates decrease months after uterine rupture, the researchers speculated that uterine rupture may alter physicians’ risk perceptions or reduce their risk tolerance, which could lead to lower TOLAC and trial of labor success rates. A reduction in these rates could lead to an increase in repeat cesarean delivery rates. The results showed that women who had experienced a previous cesarean section had a lower trial of labor success rate and a lower VBAC rate after severe uterine rupture despite a stable TOLAC rate. This suggests that physicians may change their minds about the risks when trying for labor after experiencing a cesarean section, and subsequently change the way they approach labor in patients with ruptured uterus. Uterine rupture may cause physicians to increase their estimate of the potential risk of uterine rupture, which in turn may make them more inclined to use cesarean delivery. However, rupture of the uterus can occur even when the appropriate method is applied, and for clinicians, rupture of the uterus itself does not provide additional information about other risks to women. This cognitive bias is called “acquired bias. Finally, physicians may focus on what women with ruptured uteri have in common with other women who have given birth. If they ignore the very low baseline risk of uterine rupture because of clinically similar patients, they may incorrectly assume that women are at high risk of uterine rupture. Because clinical decisions directly affect patient prognosis, it is important to optimize procedures and reduce cognitive bias. The occurrence of uterine rupture may lead to more unnecessary secondary cesarean deliveries. Therefore, clinical guidelines should not be rigidly enforced. However, cognitive bias is common and difficult to avoid, and care may be needed to minimize its impact. However, the study has several limitations. First, because maternal records were not available, it was not possible to determine the neonatal mortality or morbidity due to severe uterine rupture. It could be argued that cases of uterine rupture that result in neonatal injury or death are more influential in decision making. If so, the study may have underestimated the true impact. The researchers used the previous International Classification of Diseases (Ninth Revision) to determine deliveries. This may have led to an underestimation of TOLAC rates and an overestimation of trial-of-labor success rates. Bias in delivery measurement should not have an impact on outcome estimates. In addition, by measuring birth events, the delivery rate also includes a small group of women who go into labor but have the intention of having another cesarean section. However, this small cohort size does not affect the findings. The investigators chose to conduct the analysis at the hospital rather than at the clinical level because it is not possible to know whether the clinical delivery records document the full range of care, simply perform an emergency cesarean section or also involve some other role. Therefore, some changes that occurred in a short period of time may have gone unnoticed. In summary, recent adverse events may influence medical decisions, and physicians may be more hesitant to perform a cesarean section in a woman who has had one before. By recognizing that adverse events may influence risk assessment, physicians are able to increase awareness of these cognitive biases and move toward optimal decision making in the face of high uncertainty.