Understanding the new trends in pedestrian injury distribution and mechanism through data linkage and modeling.

The objectives of this study were to 1) collect and analyze recent pedestrian crash cases for better understanding of the pedestrian injury distribution and mechanism, 2) use computational simulations to reconstruct pedestrian cases and estimate potential benefit of pedestrian automatic emergency braking (PedAEB) in reducing pedestrian injury risks, and 3) estimate how future pedestrian crash distribution might influence priorities for pedestrian protection. Analyses of national crash-injury dataset showed that the overall number of pedestrians in crashes as well as the serious and fatal pedestrian injuries in the U.S. have been increasing in recent years. Striking vehicle type has changed (i.e., decreased proportion of passenger cars and increase of SUVs and pickup trucks) from 20 years ago mirroring changes in the fleet distribution of vehicle sales. A total of 432 pedestrian injury cases were generated by linking the Michigan trauma data and police-reported crash data from 2013 to 2018. Among the linked cases, pickup trucks and SUVs were involved in crashes with more injuries across body regions. Notably, AIS 3+ chest injuries occur at almost the same rate as lower extremity injuries. A method, combining MADYMO simulations (n = 3,500), response surface model, and data mining, was developed to reconstruct 25 linked pedestrian crash cases to estimate the effectiveness of PedAEB. Based on national field data and MADYMO simulations, PedAEB was estimated to be effective in reducing the risk of head and lower extremity injuries but is relatively less effective in reducing the risk of chest injuries. The increased proportions of SUVs and pickup trucks in the vehicle fleet and the higher penetration of PedAEB may highlight the importance of future research into chest injury risk for pedestrian protection.

Evaluation of the predicted vs. achieved occlusal outcomes with the Invisalign® appliance: A retrospective investigation of adult patients.

INTRODUCTION: The aim of this study was to investigate the relationship between occlusal contacts, overbite, transverse expansion, and the buccolingual inclination of the teeth with reference to the predicted treatment outcomes and achieved outcomes related to the use of the Invisalign® appliance in mild-to-moderate Class I malocclusions. MATERIAL AND METHODS: The occlusal contacts, overbite, the buccolingual inclination and transverse expansion of the maxillary arch of adult patients satisfying inclusion and exclusion criteria were measured at the initial (pre-treatment), predicted, and achieved treatment stages using metrology software. Pearson correlation coefficients and regression equations were calculated to determine the association between the initial, predicted and achieved changes in occlusal contact against the other variables. RESULTS: Thirty-three patients, who commenced treatment between 2013 and 2018 and satisfied inclusion/exclusion criteria were evaluated. An overall loss of posterior contact was recorded and highlighted by a significantly greater loss of contact from the maxillary buccal occlusal surfaces compared to the palatal occlusal surfaces. The mean [SD] achieved overbite outcome (2.94mm [1.17]) was greater than the predicted (1.74mm [0.87), P<0.001). The buccolingual inclination was significantly increased for the lateral incisors and first and second molars despite a predicted decrease (P≤0.007). Achieved transverse expansion showed significant variation from the predicted. The loss of posterior occlusal contact was correlated with the buccolingual inclination (r=0.70) and transverse expansion (r=0.74) of the posterior teeth. CONCLUSIONS: In mild-to-moderate Class I malocclusions, treatment using the Invisalign® appliance resulted in an overall loss of posterior contact. The loss of occlusal contact was correlated with deficiencies in achieved buccolingual inclination and transverse expansion of the posterior teeth. Planned bodily expansion was ineffective as most expansion occurred due to unplanned buccal tipping.

Occlusal contacts and treatment with the Invisalign appliance: a retrospective analysis of predicted vs achieved outcomes.

OBJECTIVES: To quantify the predicted occlusal contact outcomes compared with the clinically achieved occlusal contacts following treatment using the Invisalign aligner appliance. MATERIALS AND METHODS: The occlusal contacts of 33 adult patients presenting with a Class I mild-to-moderate malocclusion (spacing <4 mm or crowding of <6 mm) and treated using the Invisalign appliance were measured at the initial, predicted, and achieved stages of treatment by the metrology software Geomagic Control X. Assessed measurements were related to individual teeth and anterior, posterior, and overall contacts. RESULTS: The mean (standard deviation) difference between the achieved occlusal contact was significantly less than that predicted for overall occlusal contact and posterior occlusal contact (P < .0025). The achieved posterior occlusal contact was also less than pretreatment initial posterior occlusal contact. There was no difference in anterior occlusal contact between the predicted and achieved outcomes (P > .05). The central and lateral incisors displayed no statistically significant difference between the predicted and achieved occlusal contact. The patients with prescribed overcorrection demonstrated a statistically significant difference in predicted occlusal contact compared with those with nonprescribed overcorrection (P ≤ .0025), but no statistically significant difference in achieved occlusal contact. CONCLUSIONS: Treatment by the Invisalign appliance in Class I mild-to-moderate malocclusion resulted in a decrease in posterior occlusal contact. Further research is required to account for the deficiencies between the predicted and achieved clinical outcome related to occlusal contact and to determine the corrective changes required in the treatment protocols.

Classification of motor vehicle crash injury severity: A hybrid approach for imbalanced data.

This study aims to classify the injury severity in motor-vehicle crashes with both high accuracy and sensitivity rates. The dataset used in this study contains 297,113 vehicle crashes, obtained from the Michigan Traffic Crash Facts (MTCF) dataset, from 2016-2017. Similar to any other crash dataset, different accident severity classes are not equally represented in MTCF. To account for the imbalanced classes, several techniques have been used, including under-sampling and over-sampling. Using five classification learning models (i.e., Logistic regression, Decision tree, Neural network, Gradient boosting model, and Naïve Bayes classifier), we classify the levels of injury severity and attempt to improve the classification performance by two training-testing methods including Bootstrap aggregation (or bagging) and majority voting. Furthermore, due to the imbalance present in the dataset, we use the geometric mean (G-mean) to evaluate the classification performance. We show that the classification performance is the highest when bagging is used with decision trees, with over-sampling treatment for imbalanced data. The effect of treatments for the imbalanced data is maximized when under-sampling is combined with bagging. In addition to the original five classes of injury severity in the MTCF dataset, we consider two additional classification problems, one with two classes and the other with three classes, to (1) investigate the impact of the number of classes on the performance of classification models, and (2) enable comparing our results with the literature.

Periodontal Referral Patterns in Australia: 2000 Versus 2015.

BACKGROUND: A previous study investigated differences in the severity of periodontal disease on referral for specialist care between 1980 and 2000 across the United States. The present study finds patients referred in 2000 had greater severity of periodontal disease than those referred 20 years ago. METHODS: A retrospective analysis of 384 charts was completed from three periodontal practices across the east coast of Australia. Two time periods were investigated: 2000 to 2001 and 2015 to 2016. From these charts, the following data were recorded: 1) age; 2) sex; 3) smoking status; 4) number of missing teeth; and 5) number of teeth planned for extraction as part of a treatment plan at initial examination. Additionally, degree of periodontal severity was determined and recorded. The classification system was based on that used by the previous study, wherein case Types I to IV defined increasing severity of periodontal disease, and Type V defined referrals for needs other than periodontal disease (e.g., crown lengthening and implants). RESULTS: Overall, patients seen in 2015 presented with a greater percentage of Type IV and Type V cases. Smoking prevalence reduced significantly across the three locations between the two time periods. No consistent or noteworthy trends were identified with regard to number of missing teeth or number of teeth planned for extraction. CONCLUSIONS: Consistent with the results of a similar previous study, there appears to be a trend for more severe periodontal conditions being referred to periodontists. This is concerning given that more severe periodontal conditions tend to be more difficult to manage conservatively and reliably.

The Impact of Michigan's Partial Repeal of the Universal Motorcycle Helmet Law on Helmet Use, Fatalities, and Head Injuries.

OBJECTIVES: To evaluate the impact of the partial repeal of Michigan's universal motorcycle helmet law on helmet use, fatalities, and head injuries. METHODS: We compared helmet use rates and motorcycle crash fatality risk for the 12 months before and after the April 13, 2012, repeal with a statewide police-reported crash data set. We linked police-reported crashes to injured riders in a statewide trauma registry. We compared head injury before and after the repeal. Regression examined the effect of helmet use on fatality and head injury risk. RESULTS: Helmet use decreased in crash (93.2% vs 70.8%; P < .001) and trauma data (91.1% vs 66.2%; P < .001) after the repeal. Although fatalities did not change overall (3.3% vs 3.2%; P = .87), head injuries (43.4% vs 49.6%; P < .05) and neurosurgical intervention increased (3.7% vs 6.5%; P < .05). Male gender (adjusted odds ratio [AOR] = 1.65), helmet nonuse (AOR = 1.84), alcohol intoxication (AOR = 11.31), intersection crashes (AOR = 1.62), and crashes at higher speed limits (AOR = 1.04) increased fatality risk. Helmet nonuse (AOR = 2.31) and alcohol intoxication (AOR = 2.81) increased odds of head injury. CONCLUSIONS: Michigan's helmet law repeal resulted in a 24% to 27% helmet use decline among riders in crashes and a 14% increase in head injury.