Exploration and development of crash modification factors and functions for single and multiple treatments

Traffic safety is a major concern for the public, and it is an important component of the roadway management strategy. In order to improve highway safety, extensive efforts have been made by researchers, transportation engineers, Federal, State, and local government officials. With these consistent efforts, both fatality and injury rates from road traffic crashes in the United States have been steadily declining over the last six years (2006~2011). However, according to the National Highway Traffic Safety Administration (NHTSA, 2013), 33,561 people died in motor vehicle traffic crashes in the United States in 2012, compared to 32,479 in 2011, and it is the first increase in fatalities since 2005. Moreover, in 2012, an estimated 2.36 million people were injured in motor vehicle traffic crashes, compared to 2.22 million in 2011. Due to the demand of highway safety improvements through systematic analysis of specific roadway cross-section elements and treatments, the Highway Safety Manual (HSM) (AASHTO, 2010) was developed by the Transportation Research Board (TRB) to introduce a science-based technical approach for safety analysis. One of the main parts in the HSM, Part D, contains crash modification factors (CMFs) for various treatments on roadway segments and at intersections. A CMF is a factor that can estimate potential changes in crash frequency as a result of implementing a specific treatment (or countermeasure). CMFs in Part D have been developed using high-quality observational before-after studies that account for the regression to the mean threat. Observational before-after studies are the most common methods for evaluating safety effectiveness and calculating CMFs of specific roadway treatments. Moreover, cross-sectional method has commonly been used to derive CMFs since it is easier to collect the data compared to before-after methods. Although various CMFs have been calculated and introduced in the HSM, still there are critical limitations that are required to be investigated. First, the HSM provides various CMFs for single treatments, but not CMFs for multiple treatments to roadway segments. The HSM suggests that CMFs are multiplied to estimate the combined safety effects of single treatments. However, the HSM cautions that the multiplication of the CMFs may over- or under-estimate combined effects of multiple treatments. In this dissertation, several methodologies are proposed to estimate more reliable combined safety effects in both observational before-after studies and the cross-sectional method. Averaging two best combining methods is suggested to use to account for the effects of over- or under- estimation. Moreover, it is recommended to develop adjustment factor and function (i.e. weighting factor and function) to apply to estimate more accurate safety performance in assessing safety effects of multiple treatments. The multivariate adaptive regression splines (MARS) modeling is proposed to avoid the over-estimation problem through consideration of interaction impacts between variables in this dissertation. Second, the variation of CMFs with different roadway characteristics among treated sites over time is ignored because the CMF is a fixed value that represents the overall safety effect of the treatment for all treated sites for specific time periods. Recently, few studies developed crash modification functions (CMFunctions) to overcome this limitation. However, although previous studies assessed the effect of a specific single variable such as AADT on the CMFs, there is a lack of prior studies on the variation in the safety effects of treated sites with different multiple roadway characteristics over time. In this study, adopting various multivariate linear and nonlinear modeling techniques is suggested to develop CMFunctions. Multiple linear regression modeling can be utilized to consider different multiple roadway characteristics. To reflect nonlinearity of predictors, a regression model with nonlinearizing link function needs to be developed. The Bayesian approach can also be adopted due to its strength to avoid the problem of over fitting that occurs when the number of observations is limited and the number of variables is large. Moreover, two data mining techniques (i.e. gradient boosting and MARS) are suggested to use 1) to achieve better performance of CMFunctions with consideration of variable importance, and 2) to reflect both nonlinear trend of predictors and interaction impacts between variables at the same time. Third, the nonlinearity of variables in the cross-sectional method is not discussed in the HSM. Generally, the cross-sectional method is also known as safety performance functions (SPFs) and generalized linear model (GLM) is applied to estimate SPFs. However, the estimated CMFs from GLM cannot account for the nonlinear effect of the treatment since the coefficients in the GLM are assumed to be fixed. In this dissertation, applications of using generalized nonlinear model (GNM) and MARS in the cross-sectional method are proposed. In GNMs, the nonlinear effects of independent variables to crash analysis can be captured by the development of nonlinearizing link function. Moreover, the MARS accommodate nonlinearity of independent variables and interaction effects for complex data structures. In this dissertation, the CMFs and CMFunctions are estimated for various single and combination of treatments for different roadway types (e.g. rural two-lane, rural multi-lane roadways, urban arterials, freeways, etc.) as below: 1) Treatments for mainline of roadway: - adding a thru lane, conversion of 4-lane undivided roadways to 3-lane with two-way left turn lane (TWLTL) 2) Treatments for roadway shoulder: - installing shoulder rumble strips, widening shoulder width, adding bike lanes, changing bike lane width, installing roadside barriers 3) Treatments related to roadside features: - decrease density of driveways, decrease density of roadside poles, increase distance to roadside poles, increase distance to trees Expected contributions of this study are to 1) suggest approaches to estimate more reliable safety effects of multiple treatments, 2) propose methodologies to develop CMFunctions to assess the variation of CMFs with different characteristics among treated sites, and 3) recommend applications of using GNM and MARS to simultaneously consider the interaction impact of more than one variables and nonlinearity of predictors. Finally, potential relevant applications beyond the scope of this research but worth investigation in the future are discussed in this dissertation

GRAM: Fast Fine-tuning of Pre-trained Language Models for Content-based Collaborative Filtering

Content-based collaborative filtering (CCF) provides personalized item recommendations based on both users' interaction history and items' content information. Recently, pre-trained language models (PLM) have been used to extract high-quality item encodings for CCF. However, it is resource-intensive to finetune PLM in an end-to-end (E2E) manner in CCF due to its multi-modal nature: optimization involves redundant content encoding for interactions from users. For this, we propose GRAM (GRadient Accumulation for Multi-modality): (1) Single-step GRAM which aggregates gradients for each item while maintaining theoretical equivalence with E2E, and (2) Multi-step GRAM which further accumulates gradients across multiple training steps, with less than 40\% GPU memory footprint of E2E. We empirically confirm that GRAM achieves a remarkable boost in training efficiency based on five datasets from two task domains of Knowledge Tracing and News Recommendation, where single-step and multi-step GRAM achieve 4x and 45x training speedup on average, respectively.Comment: NAACL 2022 Main Conferenc

Early statin use in ischemic stroke patients treated with recanalization therapy: retrospective observational study

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.Abstract Background We aimed to determine whether early statin use following recanalization therapy improves the functional outcome of ischemic stroke. Methods Using a prospective stroke registry database, we identified a consecutive 337 patients within 6 h of onset who had symptomatic stenosis or occlusion of major cerebral arteries and received recanalization therapy. Based on commencement of statin therapy, patients were categorized into administration on the first (D1, 13.4 %), second (D2, 20.8 %) and third day or later (D ≥ 3, 15.4 %) after recanalization therapy, and no use (NU, 50.4 %). The primary efficacy outcome was a 3-month modified Rankin Scale score of 0–1, and the secondary outcomes were neurologic improvement, neurologic deterioration and symptomatic hemorrhagic transformation during hospitalization. Results Earlier use of statin was associated with a better primary outcome in a dose-response relationship (P for trend = 0.01) independent of premorbid statin use, stroke history, atrial fibrillation, stroke subtype, calendar year, and methods of recanalization therapy. The odds of a better primary outcome increased in D1 compared to NU (adjusted odds ratio, 2.96; 95 % confidence interval, 1.19–7.37). Earlier statin use was significantly associated with less neurologic deterioration and symptomatic hemorrhagic transformation in bivariate analyses but not in multivariable analyses. Interaction analysis revealed that the effect of early statin use was not altered by stroke subtype and recanalization modality (P for interaction = 0.97 and 0.26, respectively). Conclusion Early statin use after recanalization therapy in ischemic stroke may improve the likelihood of a better functional outcome without increasing the risk of intracranial hemorrhage

Reliability and Validity of the Korean Cancer Pain Assessment Tool (KCPAT)

The Korean Cancer Pain Assessment Tool (KCPAT), which was developed in 2003, consists of questions concerning the location of pain, the nature of pain, the present pain intensity, the symptoms associated with the pain, and psychosocial/spiritual pain assessments. This study was carried out to evaluate the reliability and validity of the KCPAT. A stratified, proportional-quota, clustered, systematic sampling procedure was used. The study population (903 cancer patients) was 1% of the target population (90,252 cancer patients). A total of 314 (34.8%) questionnaires were collected. The results showed that the average pain score (5 point on Likert scale) according to the cancer type and the at-present average pain score (VAS, 0-10) were correlated (r=0.56, p<0.0001), and showed moderate agreement (kappa=0.364). The mean satisfaction score was 3.8 (1-5). The average time to complete the questionnaire was 8.9 min. In conclusion, the KCPAT is a reliable and valid instrument for assessing cancer pain in Koreans

Clinical significance of HER2-low expression in early breast cancer: a nationwide study from the Korean Breast Cancer Society

There is an increasing interest in HER2-low breast cancer with promising data from clinical trials using novel anti-HER2 antibody–drug conjugates. We explored the differences in clinicopathological characteristics and survival outcomes between HER2-low and HER2-IHC 0 breast cancer. Using nationwide data from the Korean Breast Cancer Registry between 2006 and 2011, 30,491 patients with stages I to III breast cancer were included in the analysis: 9,506 (31.2%) in the HER2-low group and 20,985 (68.8%) in the HER2-IHC 0 group. Kaplan–Meier and Cox proportional hazards regression survival analysis were used to compare breast cancer-specific survival between the two groups. HER2-low breast cancer was more frequent in patients with hormone receptor-positive breast cancer than in those with triple-negative breast cancer. In patients with hormone receptor-positive breast cancer, HER2-low breast cancer was associated with fewer T4 tumors, higher histological grade, and a negative lymphatic invasion. In patients with triple-negative breast cancer, HER2-low breast cancer was associated with a high lymph node ratio and positive lymphatic invasion. HER2-low breast cancer was significantly associated with a lower Ki-67 labeling index. No significant difference was observed in overall survival between the two groups. HER2-low breast cancer showed significantly better breast cancer-specific survival than HER2-IHC 0 breast cancer, regardless of the hormone receptor status. In multivariate analysis, the impact of low HER2 expression on breast cancer-specific survival was significant only in triple-negative breast cancer (HRs, 0.68; 95% CI, 0.49–0.93; P = 0.019). These findings suggest that the biology and clinical impact of low HER2 expression can differ according to the hormone receptor status and support the need for further investigation on the understanding of the biology of HER2-low breast cancer

Effect of pre-stroke statin use on stroke severity and early functional recovery: a retrospective cohort study

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.Abstract Background Experimental studies suggest that pre-stroke statin treatment has a dual effect of neuroprotection during ischemia and neurorestoration after ischemic injury. The aim of this study was to evaluate the effect of pre-stroke statin use on initial stroke severity and early clinical outcome. Methods We used a prospective database enrolling patients with acute ischemic stroke from 12 hospitals in Korea between April 2008 and January 2012. Primary endpoint was the initial stroke severity as measured by the National Institutes of Health Stroke Scale (NIHSS) score. Secondary endpoints were good outcome (modified Rankin Scale [mRS], 0–2) and overall mRS distribution at discharge. Multivariable regression model and propensity score (PS) matching were used for statistical analyses. Results Among the 8340 patients included in this study, 964 patients (11.6 %) were pre-stroke statin users. The initial NIHSS score (mean [95 % CI]) was lower among pre-stroke statin users vs. non-users in multivariable analysis (5.7 [5.2–6.3] versus 6.4 [5.9–6.9], p = 0.002) and PS analysis (5.2 [4.7–5.7] versus 5.7 [5.4–6.0], p = 0.043). Pre-stroke statin use was associated with increased achievement of mRS 0–2 outcome (multivariable analysis: OR [95 % CI], 1.55 [1.25–1.92], p < 0.001; PS matching: OR [95 % CI], 1.47 [1.16-1.88]; p = 0.002) and favorable shift on the overall mRS distribution (multivariable analysis: OR [95 % CI], 1.29 [1.12-1.51], p = 0.001; PS matching: OR [95 % CI], 1.31 [1.11-1.54]; p = 0.001). Conclusions Pre-stroke statin use was independently associated with lesser stroke severity at presentation and better early functional recovery in patients with acute ischemic stroke

Prevalence of Neuropathic Pain and Patient-Reported Outcomes in Korean Adults with Chronic Low Back Pain Resulting from Neuropathic Low Back Pain

Study DesignA noninterventional, multicenter, cross-sectional study.PurposeWe investigated the prevalence of neuropathic pain (NP) and patient-reported outcomes (PROs) of the quality of life (QoL) and functional disability in Korean adults with chronic low back pain (CLBP).Overview of LiteratureAmong patients with CLBP, 20%–55% had NP.MethodsPatients older than 20 years with CLBP lasting for longer than three months, with a visual analog scale (VAS) pain score higher than four, and with pain medications being used for at least four weeks before enrollment were recruited from 27 general hospitals between December 2014 and May 2015. Medical chart reviews were performed to collect demographic/clinical features and diagnosis of NP (douleur neuropathique 4, DN4). The QoL (EuroQoL 5-dimension, EQ-5D; EQ-VAS) and functional disability (Quebec Back Pain Disability Scale, QBPDS) were determined through patient surveys. Multiple linear regression analyses were performed to compare PROs between the NP (DN4≥4) and non-NP (DN4<4) groups.ResultsA total of 1,200 patients (females: 65.7%; mean age: 63.4±13.0 years) were enrolled. The mean scores of EQ-5D, EQ-VAS, and QBPDS were 0.5±0.3, 55.7±19.4, and 40.4±21.1, respectively. Among all patients, 492 (41.0%; 95% confidence interval, 38.2%–43.8%) suffered from NP. The prevalence of NP was higher in male patients (46.8%; p<0.01), in patients who had pain based on radiological and neurological findings (59.0%; p<0.01), and in patients who had severe pain (49.0%; p<0.01). There were significant mean differences in EQ-5D (NP group vs. non-NP group: 0.4±0.3 vs. 0.5±0.3; p<0.01) and QBPDS (NP group vs. non-NP group: 45.8±21.2 vs. 36.3±20.2; p<0.01) scores. In the multiple linear regression, patients with NP showed lower EQ-5D (β=−0.1; p<0.01) and higher QBPDS (β=7.0; p<0.01) scores than those without NP.ConclusionsNP was highly prevalent in Korean patients with CLBP. Patients with CLBP having NP had a lower QoL and more severe dysfunction than those without NP. To enhance the QoL and functional status of patients with CLBP, this study highlights the importance of appropriately diagnosing and treating NP

Multilevel Mixed-Effects Models to Identify Contributing Factors on Freight Vehicle Crash Severity

Freight vehicle crashes are more serious than regular vehicle crashes because they are likely to lead to major damage and injury once they occur; therefore, countermeasures are needed. The fatality rate from freight vehicle crashes is 1.5 times higher than that of all other accidents, and the death rate from expressway freight vehicle crashes continues to increase. In this study, the ten-freight-vehicle crash severity models (the ordered logit and probit model, the multinomial logit and probit model, mixed-effects logit and probit model, random-effects ordered logit and probit model, and multilevel mixed-effects ordered logit and probit model) are used to analyze the freight vehicle crash severity factors. The model was constructed using data collected from expressways over eight years, and 13 factors were derived to increase the severity of crashes and 7 factors to reduce the severity of crashes. As a result of comparing the 10 constructed models using AIC and BIC, the multilevel mixed-effects ordered probit model showed the best performance. It is expected that it can contribute to improving the safety of freight vehicles in the expressway section by utilizing factors related to the severity of crashes derived from this study