Incidence of Middle Mesial Canals in Mandibular Molars during Root Canal Treatment and Nonsurgical Retreatment.

INTRODUCTION: Large-scale clinical studies on the incidence of middle mesial canals (MMCs) in nonsurgical root canal treatment (RCT) and retreatment of mandibular molars are lacking. The primary aim of this observational study was to determine the incidence of MMCs in mandibular first and second molars that received RCT or nonsurgical retreatment (NSRetx). The secondary aim was to determine factors associated with incidence of MMCs. METHODS: Included were 3018 mandibular molars that received RCT (n = 1624) or NSRetx (n = 1394) by 3 endodontists in a private practice. Demographic and procedural data were collected. Bivariate and multivariable (Poisson regression model) analyses were performed. RESULTS: Incidence of MMCs was 8.8% (n = 267). Although the bivariate analysis showed that NSRetx was significantly associated with incidence of MMCs, this association did not reach the threshold for statistical significance after controlling for all covariates (P = .07). Multivariable analysis on the entire cohort showed that incidence of MMCs was significantly associated with younger age (risk ratio [RR] = 1.62; 95% confidence interval [CI], 1.28-2.06), male gender (RR = 1.48; 95% CI, 1.18-1.85), preoperative cone-beam computed tomography (CBCT) (RR = 1.48; 95% CI, 1.17-1.89), and first molar (RR = 2.30; 95% CI:1.74-3.05). Subgroup multivariable analyses revealed that incidence of MMCs was associated with male gender only in the RCT group (RR = 2.26; 95% CI, 1.55-3.30) but not in the NSRetx group (RR = 1.11; 95% CI, 0.82, 1.50); and with preoperative CBCT only in the NSRetx group (RR = 1.78; 95% CI, 1.28, 2.50) but not in the RCT group (RR = 1.10; 95% CI, 0.71, 1.69). Associations with younger age and first molar remained unchanged. CONCLUSIONS: Overall incidence of MMCs was 8.8%. Incidence of MMCs was significantly higher in younger patients and in mandibular first molars but was not associated with the type of treatment.

Flare-ups After Nonsurgical Retreatments: Incidence, Associated Factors, and Prediction.

INTRODUCTION: The aims of this study were to determine the incidence, associated factors, and predictability of flare-ups after nonsurgical retreatments (NSRetx). METHODS: All patients (n = 3,666) who received NSRetx by 3 endodontists in 2 endodontic offices during a period of 6 years (2016-2022) were included. Demographic, diagnostic, and procedural data were collected. A flare-up was defined as moderate-severe pain with/without swelling that took place within 14 days of initiation of NSRetx and resulted in an unscheduled appointment. Bivariate analyses on the entire cohort and multivariable analyses on cases with complete dataset (n = 2,846) were performed to identify significant associations. A Random Forest algorithm was used to make a prediction model. RESULTS: Incidence of flare-ups was 3.95% (n = 145). Increased risk of flare-ups was associated with diabetes (adjusted odds ratio [AOR] = 2.01; 95% confidence interval [CI], 1.01-3.97), mandibular teeth (AOR = 1.67; 95% CI, 1.11-2.52), moderate-severe preoperative pain on percussion (AOR = 2.13; 95% CI, 1.31-3.50), and pain on palpation (AOR = 1.63; 95% CI, 1.01-2.64). Lower risk of flare-up was associated with high blood pressure (AOR = 0.44; 95% CI, 0.21-0.82) (P < .05). The prediction model was not able to predict flare-ups with sufficient confidence (precision = 0.13). CONCLUSIONS: Incidence of flare-ups following NSRetx was low (∼4%) and had a weak but significant association with the history of diabetes. Moderate-severe pain on percussion, pain on palpation (any intensity), and mandibular teeth had a significant association with flare-ups. The history of high blood pressure was associated with lower risk of flare-ups. Flare-ups were difficult to predict in this clinical setting.

Limiting adverse birth outcomes in resource-limited settings (LABOR): protocol of a prospective intrapartum cohort study.

Background: Each year, nearly 300,000 women and 5 million fetuses or neonates die during childbirth or shortly thereafter, a burden concentrated disproportionately in low- and middle-income countries. Identifying women and their fetuses at risk for intrapartum-related morbidity and death could facilitate early intervention. Methods: The Limiting Adverse Birth Outcomes in Resource-Limited Settings (LABOR) Study is a multi-country, prospective, observational cohort designed to exhaustively document the course and outcomes of labor, delivery, and the immediate postpartum period in settings where adverse outcomes are frequent. The study is conducted at four hospitals across three countries in Ghana, India, and Zambia. We will enroll approximately 12,000 women at presentation to the hospital for delivery and follow them and their fetuses/newborns throughout their labor and delivery course, postpartum hospitalization, and up to 42 days thereafter. The co-primary outcomes are composites of maternal (death, hemorrhage, hypertensive disorders, infection) and fetal/neonatal adverse events (death, encephalopathy, sepsis) that may be attributed to the intrapartum period. The study collects extensive physiologic data through the use of physiologic sensors and employs medical scribes to document examination findings, diagnoses, medications, and other interventions in real time. Discussion: The goal of this research is to produce a large, sharable dataset that can be used to build statistical algorithms to prospectively stratify parturients according to their risk of adverse outcomes. We anticipate this research will inform the development of new tools to reduce peripartum morbidity and mortality in low-resource settings.

Beyond Two Cultures: Cultural Infrastructure for Data-driven Decision Support.

In the twenty years since Dr. Leo Breiman's incendiary paper Statistical Modeling: The Two Cultures was first published, algorithmic modeling techniques have gone from controversial to commonplace in the statistical community. While the widespread adoption of these methods as part of the contemporary statistician's toolkit is a testament to Dr. Breiman's vision, the number of high-profile failures of algorithmic models suggests that Dr. Breiman's final remark that "the emphasis needs to be on the problem and the data" has been less widely heeded. In the spirit of Dr. Breiman, we detail an emerging research community in statistics - data-driven decision support. We assert that to realize the full potential of decision support, broadly and in the context of precision health, will require a culture of social awareness and accountability, in addition to ongoing attention towards complex technical challenges.