Categories Of Likelihood Research Articles

Cost effective utilization of apheresis for stem cell transplantation in multiple myeloma.

e19528 Background: Salvage autologous stem cell transplantation (ASCT) is an effective and feasible treatment option for patients with relapsed multiple myeloma (MM). It is standard practice to collect stem cells adequate for two transplants for ASCT eligible patients with newly diagnosed MM (NDMM). Stem cell collection is an expensive procedure due to the cost of granulocyte-colony stimulating factor, plerixafor, apheresis, chemotherapy in select cases and finally cryopreservation. However, the rate of utilization of second ASCT is not known. Methods: We conducted a single institution, retrospective study of 511 consecutive patients with NDMM who underwent ASCT to identify the proportion of patients receiving second ASCT. Patients received ASCT as front-line therapy between 1/1/2015-2/13/2019. Demographic and disease characteristics were summarized using median and range for continuous variables, and frequency and percentages were calculated for categorical variables. The cumulative incidence of second ASCT was estimated using the Fine and Gray method, and competing risk regression models were used to estimate the hazard ratios for risk of second ASCT. All statistical tests were two-sided with a type 1 error of < 0.05 indicating statistical significance. All estimates were reported with 95% confidence intervals (95% CI). Results: At a median follow-up of 4.7 years, 25 out of 511 patients received a second ASCT. The cumulative incidence rate at 5 years was 2.6% (95% CI 1.4-4.4%).Age at diagnosis, age at first ASCT and Charlson comorbidity index (CMI) were significantly associated with the risk of second ASCT in univariable analysis (UVA). In multivariable analysis (MVA) age at first ASCT was the only factor that was predictive of a second ASCT. We devised a predictive score for utilization of second ASCT using hazard ratios for each of the three significant factors noted in the UVA. Scoring was done as follows: age < 65 years – 1 point, > 65 years – 7 points; 1 point for CMI 0-1, 2 points for CMI 2 or higher; serum creatinine ≤1.2 – 1 point, > 1.2 – 3 points. Based on the total score, patients are grouped into low ( < = 3), medium (4-6) and high (7+) likelihood categories, with cumulative incidence rates of receiving second ASCT at 4.0% (95% CI: 1.3-9.1%), 3.4% (95% CI 1.4-7.0%) and 0.6% (95% CI 0.1-3.1%) respectively. Patients older than 65 years were 87% less likely to receive a second ASCT compared with those younger than 65 years (HR = 0.13, 95% CI 0.02-0.98). Conclusions: Using this predictive score, goal of stem cell collection for patients with MM could be determined more efficiently. This could avoid extra days of apheresis, chemo mobilization, hospitalizations and need for cryopreservation at least for patients with a low likelihood of second ASCT, translating into cost effective utilization of resources. We plan to validate this score prospectively in a larger group of patients with NDMM undergoing ASCT in the future.

Predictive Score for Utilization of Second Autologous Stem Cell Transplantation in Patients with Multiple Myeloma

Introduction: Salvage autologous stem cell transplantation (ASCT) is an effective and feasible treatment option for patients with relapsed multiple myeloma (MM). It is standard practice to collect stem cells adequate for two transplants for ASCT eligible patients with newly diagnosed multiple myeloma (NDMM). Apheresis is an expensive procedure with the added risk of complications from central venous access such as bleeding, infection and, granulocyte colony stimulating factor administration. However, the rate of utilization of second ASCT is not known. Methods: We conducted a single institution, retrospective study of 511 consecutive patients with NDMM who underwent ASCT to identify factors predictive of receiving second ASCT. Patients received ASCT as front-line therapy between 1/1/2015-2/13/2019. Demographic and disease characteristics were summarized using median and range for continuous variables, and frequency and percentages were calculated for categorical variables. The cumulative incidence of second ASCT was estimated using the Fine and Gray method, and competing risk regression models were used to estimate the hazard ratios for risk of second ASCT. Analyses were performed using Stata version 18 (StataCorp, College Station, TX), and all statistical tests were two-sided with a type 1 error of <0.05 indicating statistical significance. All estimates were reported with 95% confidence intervals (95% CI). Results: At a median follow-up was 4.7 years, 25 out of 511 patients received a second ASCT. The cumulative incidence rate at 5 years was 2.6% (95% CI 1.4-4.4%) (Figure 1).Age at diagnosis, age at first ASCT and Charlson comorbidity index (CMI) were significantly associated with the risk of second ASCT in univariable analysis (UVA). In multivariable analysis (MVA) age at first ASCT was the only factor that was statistically significantly associated with the likelihood of a second ASCT. We devised a predictive score for utilization of second ASCT using hazard ratios for each of the three significant factors noted in the UVA. Scoring was done as follows: age <65 years - 1 point, >65 years - 7 points; 1 point for CMI 0-1, 2 points for CMI 2 or higher; serum creatinine </=1.2 - 1 point, >1.2 - 3 points. Based on the total score, patients are grouped into low (<=3), medium (4-6) and high (7+) likelihood categories, with cumulative incidence rates of receiving second ASCT at 4.0% (95% CI: 1.3-9.1%), 3.4% (95% CI 1.4-7.0%) and 0.6% (95% CI 0.1-3.1%) respectively. Patients older than 65 years were 87% less likely to receive a second ASCT compared with those younger than 65 years (HR=0.13, 95% CI 0.02-0.98). Conclusion: Using this predictive score, goal of stem cell collection for patients with MM could be determined more efficiently. This could avoid extra days of apheresis, chemo mobilization, hospitalizations and need for stem cell storage at least for patients with a low likelihood of second ASCT, translating into cost effective utilization of resources. We plan to validate this score prospectively in a larger group of patients with NDMM undergoing ASCT in the future.

Cardiac Virtual Noncontrast Images for Calcium Quantification with Photon-counting Detector CT

Purpose To assess the accuracy of aortic valve calcium (AVC), mitral annular calcium (MAC), and coronary artery calcium (CAC) quantification and risk stratification using virtual noncontrast (VNC) images from late enhancement photon-counting detector CT as compared with true noncontrast images. Materials and Methods This retrospective, institutional review board–approved study evaluated patients undergoing photon-counting detector CT between January and September 2022. VNC images were reconstructed from late enhancement cardiac scans at 60, 70, 80, and 90 keV using quantum iterative reconstruction (QIR) strengths of 2–4. AVC, MAC, and CAC were quantified on VNC images and compared with quantification of AVC, MAC, and CAC on true noncontrast images using Bland-Altman analyses, regression models, intraclass correlation coefficients (ICC), and Wilcoxon tests. Agreement between severe aortic stenosis likelihood categories and CAC risk categories determined from VNC and true noncontrast images was assessed by weighted κ analysis. Results Ninety patients were included (mean age, 80 years ± 8 [SD]; 49 male patients). Scores were similar on true noncontrast images and VNC images at 80 keV for AVC and MAC, regardless of QIR strengths, and VNC images at 70 keV with QIR 4 for CAC (all P > .05). The best results were achieved using VNC images at 80 keV with QIR 4 for AVC (mean difference, 3; ICC = 0.992; r = 0.98) and MAC (mean difference, 6; ICC = 0.998; r = 0.99), and VNC images at 70 keV with QIR 4 for CAC (mean difference, 28; ICC = 0.996; r = 0.99). Agreement between calcification categories was excellent on VNC images at 80 keV for AVC (κ = 0.974) and on VNC images at 70 keV for CAC (κ = 0.967). Conclusion VNC images from cardiac photon-counting detector CT enables patient risk stratification and accurate quantification of AVC, MAC, and CAC. Keywords: Coronary Arteries, Aortic Valve, Mitral Valve, Aortic Stenosis, Calcifications, Photon-counting Detector CT Supplemental material is available for this article © RSNA, 2023

Predictors of Making a Referral to Child Protective Services Prior to Expert Consultation

ObjectiveSuspicion for child abuse is influenced by implicit biases. Evaluation by a Child Abuse Pediatrician (CAP) may reduce avoidable child protective services (CPS) referrals. Our objective was to investigate the association of patient demographic, social and clinical characteristics with CPS referral before consultation by a CAP (preconsultation referral). MethodsChildren<5years-old undergoing in-person CAP consultation for suspected physical abuse from February 2021 through April 2022 were identified in CAPNET, a multicenter child abuse research network. Marginal standardization implemented with logistic regression analysis examined hospital-level variation and identified demographic, social, and clinical factors associated with preconsultation referral adjusting for CAP’s final assessment of abuse likelihood. ResultsAmong the 61% (1005/1657) of cases with preconsultation referral, the CAP consultant had low concern for abuse in 38% (384/1005). Preconsultation referrals ranged from 25% to 78% of cases across 10 hospitals (P < .001). In multivariable analyses, preconsultation referral was associated with public insurance, caregiver history of CPS involvement, history of intimate partner violence, higher CAP level of concern for abuse, hospital transfer, and near-fatality (all P < .05).The difference in preconsultation referral prevalence for children with public versus private insurance was significant for children with low CAP concern for abuse (52% vs 38%) but not those with higher concern for abuse (73% vs 73%), (P = .023 for interaction of insurance and abuse likelihood category). There were no differences in preconsultation referral based on race or ethnicity. ConclusionsBiases based on socioeconomic status and social factors may impact decisions to refer to CPS before CAP consultation.

Failure Mode and Effects Analysis for Experimental Use of FLASH on a Clinical Accelerator

PurposeThe use of a linear accelerator (LINAC) in ultrahigh-dose-rate (UHDR) mode can provide a conduit for wider access to UHDR FLASH effects, sparing normal tissue, but care needs to be taken in the use of such systems to ensure errors are minimized. The failure mode and effects analysis was carried out in a team that has been involved in converting a LINAC between clinical use and UHDR experimental mode for more than 1 year after the proposed methods of TG100. Methods and MaterialsA team of 9 professionals with extensive experience were polled to outline the process map and workflow for analysis, and developed fault trees for potential errors, as well as failure modes that would result. The team scored the categories of severity magnitude, occurrence likelihood, and detectability potential in a scale of 1 to 10, so that a risk priority number (RPN = severity×occurrence×detectability) could be assessed for each. ResultsA total of 46 potential failure modes were identified, including 5 with an RPN >100. These failure modes involved (1) patient set up, (2) gating mechanisms in delivery, and (3) detector in the beam stop mechanism. The identified methods to mitigate errors included the (1) use of a checklist post conversion, (2) use of robust radiation detectors, (3) automation of quality assurance and beam consistency checks, and (4) implementation of surface guidance during beam delivery. ConclusionsThe failure mode and effects analysis process was considered critically important in this setting of a new use of a LINAC, and the expert team developed a higher level of confidence in the ability to safely move UHDR LINAC use toward expanded research access.

Prediction of Left Ventricular Ejection Fraction Change Following Treatment With Sacubitril/Valsartan

BackgroundSacubitril/valsartan (Sac/Val) improves left ventricular ejection fraction (LVEF) in heart failure (HF) with reduced ejection fraction regardless of previous treatments. Improvements in LVEF may change eligibility for primary implantable cardioverter-defibrillator (ICD) placement. Awaiting LVEF improvement may expose patients to potential risks for arrhythmic complications. ObjectivesThe authors sought to develop a model predicting LVEF change after Sac/Val therapy. MethodsA total of 416 persons with HF and LVEF of <35% were included in this analysis. Following initiation of Sac/Val, echocardiographic parameters were measured serially for 1 year. A machine learning algorithm was implemented to develop a risk model for predicting the persistence of LVEF of <35% after 1 year and was validated in a separate group of study participants. ResultsBaseline LVEF, left ventricular mass index, HF duration, age, N-terminal pro–B-type natriuretic peptide concentration at baseline and change by day 14, and body mass index were the most significant factors for identifying lack of LVEF improvement to ≥35% after 1 year. In the training and validation cohorts, the areas under the model curve for predicting lack of LVEF improvement were 0.92 and 0.86, respectively. Three categories of likelihood for LVEF of <35% after 1 year of Sac/Val treatment were developed based on the model predictions: 3.8%, 30.1%, and 83.7%. During follow-up, arrhythmia event rates were 0.9%, 2.9%, and 6.7% in these groups, respectively. ConclusionsMany persons with HF with reduced ejection fraction eligible for ICD insertion experience an increase in LVEF to ≥35% after treatment with Sac/Val. Early identification of those less likely to improve their LVEF might allow for more refined selection of primary ICD candidates. (Effects of Sacubitril/Valsartan Therapy on biomarkers, Myocardial Remodeling, and Outcomes [PROVE-HF]; NCT02887183)

Evaluating Risk of FLASH Experiments on a Clinical LINAC with Failure Mode and Effects Analysis

<h3>Purpose/Objective(s)</h3> Use of clinical linear accelerators in ultra-high dose rate (UHDR) mode can provide a conduit for wider access to UHDR FLASH effects, sparing normal tissue, but care needs to be taken in the use of such systems to ensure errors are avoided. A Failure Modes and Effects Analysis (FMEA) was conducted with a team involved in converting a LINAC between clinical use and UHDR experimental mode for more than one year, following the proposed methods of AAPM (The American Association of Physicists in Medicine) Task Group 100 report. <h3>Materials/Methods</h3> A team of 9 professionals with extensive experience outlined the process map and workflow for analysis, and developed fault trees for potential errors, as well as failure modes that would results. The team scored the categories of severity magnitude (S), occurrence likelihood (O), and detectability potential (D) on a scale of 1 to 10, so that a risk priority number (RPN=S*O*D) could be assessed for each potential failure. <h3>Results</h3> A total of 46 potential failure modes were identified, including 5 with RPN>100, all during converting and experimental use in UHDR mode and none during conversion to clinical mode. These failure modes involved 1) patient set up, 2) gating mechanisms in delivery, and 3) detector in the beam stop mechanism. Identified methods to mitigate errors included 1) use of a checklist post conversion, 2) use of robust radiation detectors, 3) automation of QA and beam consistency checks, and 4) implementation of surface guidance during beam delivery. <h3>Conclusion</h3> The FMEA process was considered critically important in this setting of a new use of a LINAC, and the expert team developed a higher level of confidence in the ability to safely move UHDR LINAC use towards expanded research access.

Mobile and online consumer tools to screen for autism do not promote equity.

Many parents wonder if their child might have autism. Many parents use their smartphones to answer health questions. We asked, "How easy or hard is it for parents to use their smartphones to find 'tools' to test their child for signs of autism?" After doing pretend parent searches, we found that only one in 10 search results were tools to test children for autism. These tools were not designed for parents who have low income or other challenges such as low literacy skills, low English proficiency, or not being tech-savvy.

Identification of risk features using text mining and BERT-based models: Application to an oil refinery

The uncontrollable release of hazardous substances may lead to catastrophic accidents. In this context, risk studies are aimed at recommending either preventive measures or designing safeguards to mitigate the consequences. To that end, risk experts postulate possible leakages, then identify their causes and consequences, and finally evaluate and classify the risks into categories. These analyses rely on examination different engineering textual documents and attendance numerous meetings, which is very time consuming. Moreover, this qualitative process of hazard identification and assessment are usually the first steps of quantitative risk analysis (QRA) and is paramount to ensure its quality. Therefore, we here propose to use text mining and fine-tuned trained bidirectional encoder representations from transformers (BERT) models to support and reduce the efforts required for completing the early stages of QRA. Our idea is to apply these techniques to identify the potential consequences of accidents related to the operation of an oil refinery and classify each scenario in terms of severity of the consequence and likelihood of occurrence. The proposed method was applied to an actual oil refinery and presented very promising results. The potential consequences, the severity and likelihood categories were predicted with a mean accuracy of 97.42%, 86.44%, and 94.34% respectively. The models resulting from this research were embedded into a web-based app that is called HALO (hazard analysis based on language processing for oil refineries).

Validation of the HFA-PEFF and the H2FPEF scores for the diagnosis of heart failure with preserved ejection fraction in Japanese patients:a report from the Japanese multicentre registry

Abstract Background The standard diagnosis of heart failure (HF) with preserved ejection fraction (HFpEF) is based on the following: 1) symptoms of HF, 2) preserved left ventricular (LV) ejection fraction (LVEF, &amp;gt;50%), and 3) presence of LV diastolic dysfunction confirmed by echocardiography or cardiac catheterisation. However, there are limits to the diagnostic accuracy of individual parameters, and what cut-off values should be applied and how they should be combined remain unclear. Diagnostic algorithms for HFpEF such as the HFA-PEFF algorithm and the H2FPEF score have been proposed; however, previous validation studies were conducted in stable chronic HF and did not include an invasive haemodynamic assessment. Thus, the diagnostic accuracy for HFpEF lacked robustness. Moreover, information on their applicability in the Asian population is limited. Purpose The aim of this study was to investigate these scores' diagnostic validity for HFpEF in Japanese patients recently hospitalised due to acute decompensated HF. Methods We examined patients with HFpEF recently hospitalised with acute decompensated HF whose HFA-PEFF and H2FPEF scores could be calculated at discharge from a nationwide HFpEF-specific multicentre registry (HFpEF group) and control patients who underwent echocardiography to investigate the cause of dyspnoea in our hospital (Non-HFpEF group). We calculated the HFA-PEFF and the H2FPEF scores among the studied population. Receiver operating characteristic (ROC) curves and area under the curve (AUC) were computed to compare the diagnostic accuracy of these scores. Results The studied population included 372 consecutive patients (194 HFpEF group and 178 Non-HFpEF group; HFpEF prevalence, 52%). The HFA-PEFF score classified 155 (42%) of all patients into the high likelihood category (5–6 points) and only 19 (5%) into the low likelihood category (0–1 point). A high HFA-PEFF score could diagnose HFpEF with a high specificity of 84% and a positive predictive value (PPV) of 82%, and a low HFA-PEFF score could rule out HFpEF with a high sensitivity of 99% and a negative predictive value (NPV) of 89%. The H2FPEF score classified 86 (23%) of all patients into the high likelihood category (6–9 points) and 84 (23%) into the low likelihood category (0–1 point). HFpEF could be diagnosed with a high H2FPEF score (specificity, 97%; PPV, 94%) or ruled out with a low H2FPEF score (sensitivity, 97%; NPV, 93%). The diagnostic accuracy for the HFA-PEFF and H2FPEF scores was 0.82 (95% confidence interval [CI] 0.78–0.86) and 0.89 (95% CI 0.86–0.93), respectively, by the AUC of the ROC curve (P=0.004) (Figure 1A). In the HFA-PEFF sub-scores, the functional score showed little diagnostic value, while the morphological and biomarker scores showed moderate diagnostic value (Figure 1B). Conclusions The H2FPEF score may be more useful than the HFA-PEFF score in diagnosing HFpEF in Japanese patients. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): a Grant-in-Aid for Scientific Research from the Japan Society for the Promotion of Science (JSPS KAKENHI) Figure 1

The LiverTox Paradox-Gaps between Promised Data and Reality Check.

The LiverTox database compiles cases of idiosyncratic drug-induced liver injury (iDILI) with the promised aims to help identify hepatotoxicants and provide evidence-based information on iDILI. Weaknesses of this approach include case selection merely based on published case number and not on a strong causality assessment method such as the Roussel Uclaf Causality Assessment Method (RUCAM). The aim of this analysis was to find out whether the promised aims have been achieved by comparison of current iDILI case data with those promised in 2012 in LiverTox. First, the LiverTox criteria of likelihood categories applied to iDILI cases were analyzed regarding robustness. Second, the quality was analyzed in LiverTox cases caused by 46 selected drugs implicated in iDILI. LiverTox included iDILI cases of insufficient quality because most promised details were not fulfilled: (1) Standard liver injury definition; (2) incomplete narratives or inaccurate for alternative causes; and (3) not a single case was assessed for causality with RUCAM, as promised. Instead, causality was arbitrarily judged on the iDILI case number presented in published reports with the same drug. All of these issues characterize the paradox of LiverTox, requiring changes in the method to improve data quality and database reliability. In conclusion, establishing LiverTox is recognized as a valuable effort, but the paradox due to weaknesses between promised data quality and actual data must be settled by substantial improvements, including, for instance, clear definition and identification of iDILI cases after evaluation with RUCAM to establish a robust causality grading.

Measuring Collaboration Through Concurrent Electronic Health Record Usage: Network Analysis Study.

BackgroundCollaboration is vital within health care institutions, and it allows for the effective use of collective health care worker (HCW) expertise. Human-computer interactions involving electronic health records (EHRs) have become pervasive and act as an avenue for quantifying these collaborations using statistical and network analysis methods.ObjectiveWe aimed to measure HCW collaboration and its characteristics by analyzing concurrent EHR usage.MethodsBy extracting concurrent EHR usage events from audit log data, we defined concurrent sessions. For each HCW, we established a metric called concurrent intensity, which was the proportion of EHR activities in concurrent sessions over all EHR activities. Statistical models were used to test the differences in the concurrent intensity between HCWs. For each patient visit, starting from admission to discharge, we measured concurrent EHR usage across all HCWs, which we called temporal patterns. Again, we applied statistical models to test the differences in temporal patterns of the admission, discharge, and intermediate days of hospital stay between weekdays and weekends. Network analysis was leveraged to measure collaborative relationships among HCWs. We surveyed experts to determine if they could distinguish collaborative relationships between high and low likelihood categories derived from concurrent EHR usage. Clustering was used to aggregate concurrent activities to describe concurrent sessions. We gathered 4 months of EHR audit log data from a large academic medical center’s neonatal intensive care unit (NICU) to validate the effectiveness of our framework.ResultsThere was a significant difference (P<.001) in the concurrent intensity (proportion of concurrent activities: ranging from mean 0.07, 95% CI 0.06-0.08, to mean 0.36, 95% CI 0.18-0.54; proportion of time spent on concurrent activities: ranging from mean 0.32, 95% CI 0.20-0.44, to mean 0.76, 95% CI 0.51-1.00) between the top 13 HCW specialties who had the largest amount of time spent in EHRs. Temporal patterns between weekday and weekend periods were significantly different on admission (number of concurrent intervals per hour: 11.60 vs 0.54; P<.001) and discharge days (4.72 vs 1.54; P<.001), but not during intermediate days of hospital stay. Neonatal nurses, fellows, frontline providers, neonatologists, consultants, respiratory therapists, and ancillary and support staff had collaborative relationships. NICU professionals could distinguish high likelihood collaborative relationships from low ones at significant rates (3.54, 95% CI 3.31-4.37 vs 2.64, 95% CI 2.46-3.29; P<.001). We identified 50 clusters of concurrent activities. Over 87% of concurrent sessions could be described by a single cluster, with the remaining 13% of sessions comprising multiple clusters.ConclusionsLeveraging concurrent EHR usage workflow through audit logs to analyze HCW collaboration may improve our understanding of collaborative patient care. HCW collaboration using EHRs could potentially influence the quality of patient care, discharge timeliness, and clinician workload, stress, or burnout.

Enlarged Parathyroid Glands on Trauma Computed Tomography: Frequency and Assessment for Possible Primary Hyperparathyroidism.

The aims of the study were to determine the frequency of enlarged parathyroid glands among patients undergoing trauma computed tomography (CT) who fall within the typical primary hyperparathyroidism (PHPT) age range and to assess for evidence of PHPT. For this retrospective study of 336 emergency department patients, concurrent cervical spine CT and neck CT angiography (CTA) examinations were reviewed for visible parathyroid glands. When visible, estimated weight was calculated, and a PHPT likelihood category was assigned after medical record review. At least 1 parathyroid gland was visible in 17 patients (5%) and enlarged (estimated weight > 60 mg) in 11 (3%). Patients classified as "highly likely" or "likely" of having PHPT exhibited larger glands (median, 355 mg) than those classified as "unlikely" or "highly unlikely" (median, 47 mg; P = 0.01). Parathyroid glands were enlarged in 3% of our cohort. Although PHPT likelihood seems to increase with gland size, definitive determination requires both serum calcium and serum parathyroid hormone.

Modeling spatially resolved characterization factors for eutrophication potential in life cycle assessment.

Prior versions of the Tool for Reduction and Assessment of Chemical and other environmental Impacts (TRACI) have recognized the need for spatial variability when characterizing eutrophication. However, the method's underlying environmental models had not been updated to reflect the latest science. This new research provides the ability to differentiate locations with a high level of detail within the USA and provides global values at the country level. In previous research (Morelli et al. 2018), the authors reviewed a broad range of domain-specific models and life cycle assessment methods for characterization of eutrophication and ranked these by levels of importance to the field and readiness for further development. The current research is rooted in the decision outcome of Morelli et al. (2018) to separate freshwater and marine eutrophication to allow for the most tailored characterization of each category individually. The current research also assumes that freshwater systems are limited by phosphorus and marine systems are limited by nitrogen. Using a combination of spatial modeling methods for soil, air, and water, we calculate midpoint characterization factors for freshwater and marine eutrophication categories and evaluate the results through a US-based case application. Maps of the nutrient inventories, characterization factors, and overall impacts of the case application illustrate the spatial variation and patterns in the results. The importance of variation in geographic location is demonstrated using nutrient-based activity likelihood categories of agricultural (rural fertilizer), non-agricultural (urban fertilizer), and general (human waste processing). Proximity to large bodies of water, as well as individual hydraulic residence times, was shown to affect the comparative values of characterization factors across the USA. In this paper, we have calculated and applied finely resolved freshwater and marine eutrophication characterization factors for the USA and country-level factors for the rest of the globe. Additional research is needed to provide similarly resolved characterization factors for the entire globe, which would require expansion of publicly available data and further development of applicable fate and transport models. Further scientific advances may also be considered as computing capabilities become more sophisticated and widely accessible.

Quantitative Evaluation of 18F-Flutemetamol PET in Patients With Cognitive Impairment and Suspected Alzheimer's Disease: A Multicenter Study.

Background: In clinical practice, equivocal findings are inevitable in visual interpretation of whether amyloid positron emission tomography (PET) is positive or negative. It is therefore necessary to establish a more objective quantitative evaluation method for determining the indication for disease-modifying drugs currently under development.Aims: We aimed to determine cutoffs for positivity in quantitative analysis of 18F-flutemetamol PET in patients with cognitive impairment and suspected Alzheimer's disease (AD). We also evaluated the clinical efficacy of amyloid PET in the diagnosis of AD. This study was registered in the Japan Registry of Clinical Trials (jRCTs, 031180321).Methods: Ninety-three patients suspected of having AD underwent 18F-flutemetamol PET in seven institutions. A PET image for each patient was visually assessed and dichotomously rated as either amyloid-positive or amyloid-negative by two board-certified nuclear medicine physicians. If the two readers obtained different interpretations, the visual rating was rerun until they reached consensus. The PET images were quantitatively analyzed using the standardized uptake value ratio (SUVR) and standardized Centiloid (CL) scale with the whole cerebellum as a reference area.Results: Visual interpretation obtained 61 positive and 32 negative PET scans. Receiver operating characteristic analysis determined the best agreement of quantitative assessments and visual interpretation of PET scans to have an area under curve of 0.982 at an SUVR of 1.13 and a CL of 16. Using these cutoff values, there was high agreement between the two approaches (kappa = 0.88). Five discordant cases had SUVR and CL values ranging from 1.00 to 1.22 and from 1 to 26, respectively. In these discordant cases, either diffuse or mildly focal elevation of cortical activity confused visual interpretation. The amyloid PET outcome significantly altered the diagnosis of AD (χ2 = 51.3, p < 0.0001). PET imaging elevated the proportions of the very high likelihood category from 20.4 to 46.2% and the very low likelihood category from 0 to 22.6%.Conclusion: Quantitative analysis of amyloid PET using 18F-flutemetamol can objectively evaluate amyloid positivity using the determined cutoffs for SUVR and CL. Moreover, amyloid PET may have added value over the standard diagnostic workup in dementia patients with cognitive impairment and suspected AD.

Assessing potential likelihood and impacts of landslides on transportation network vulnerability

As one of the devastating natural disasters, landslide may induce significant losses of properties and lives area-wide, and generate dramatic damages to transportation network infrastructure. Accessing the impacts of landslide-induced disruptions to roadway infrastructure can be extremely difficult due to the complexity of involved impact factors and uncertainties of vulnerability related events. In this study, a data-driven approach is developed to assess landslide-induced transportation roadway network vulnerability and accessibility. The vulnerability analysis is conducted by integrating a series of static and dynamic factors to reflect the landslide likelihood and the consequences of network accessibility disruptions. The analytical hierarchy process (AHP) model was developed to assess and map the landslide likelihood. A generic vulnerability index (VI) was calculated for each roadway link in the network to identify critical links. Spatial distributions of landslide likelihood, consequences of network disruptions, and network vulnerability degrees were fused and analyzed. The roadway network on Oahu Island in Hawaii is utilized to demonstrate the effectiveness of the proposed approach with all the geo-coded information for its network vulnerability analysis induced by area-wide landslides. Specifically, the study area was classified into five categories of landslide likelihood: very high, high, moderate, low, and stable. About 34% of the study area was assigned as the high or very high categories. The results of network vulnerability analyses highlighted the importance of three highway segments tunnel through the Ko‘olau Range from leeward to windward, connecting Honolulu to the windward coast including the Pali highway segment, Likelike highway segment, and Interstate H-3 highway segment. The proposed network vulnerability analysis method provides a new perspective to examine the vulnerability and accessibility of the roadway network impacted by landslides.

A Revised Discrepancy Method for Identifying Dyslexia

The issue of how to reliably identify students with developmental dyslexia in order that they may serve in research studies and receive appropriate intervention has been unresolved for decades. The primary issue is how to distinguish students who are likely to have dyslexia from the considerable number of students who are simply poor readers. The present study explores the feasibility of developing a valid method for selecting students with dyslexia to serve as subjects in research studies and to enroll in special intervention programs. After consulting 16 definitions of dyslexia, five common elements were identified, and operational criteria were developed for four of the elements. These criteria were applied to 70 school-identified students with dyslexia residing in eight states. The results were used to establish three categories of likelihood for dyslexia: very likely, likely, and not very likely. According to our revised discrepancy method, 51% of the students currently receiving services under the dyslexia label satisfied the dyslexia likelihood criteria of very likely or likely. The remaining 49% did not satisfy the dyslexia likelihood criteria (i.e., they were not very likely to have dyslexia). Most researchers would probably agree that the students identified by this revised discrepancy method do in fact have dyslexia (i.e., seriously low reading ability, average or better cognitive ability, and a standard score difference of 15 to 29 points [for likely] and 30 points or more [for very likely]).

Self-Reference enhances memory for multi-element events judged likely to happen in young and older adults

ABSTRACTWe investigated whether the strategy of self-reference can benefit memory for multi-element events, a kind of relational memory that is relatively less studied but highly relevant to daily life. Young and older adults imagined different person-object-location events with reference to themselves and two famous others (i.e., George Clooney and Oprah Winfrey), rated the likelihood that each event would happen, and then completed incidental memory tests on different pairs of elements within the event. We found that self-reference enhanced memory for object-location and person-object pairs in both age groups. Such self-reference effects were observed consistently only for events rated as likely to happen. There was also an overall memory advantage for the higher-likelihood events, which did not differ between young and older adults. Further, the self-reference effects were not correlated with memory functioning in either age group. Retrieval of within-event associations showed a significant level of dependency, which did not differ as a function of reference condition or likelihood category. These findings highlight the ways in which self-reference and prior knowledge improve relational memory, and suggest that the advantage of self-reference is not attributable to increased dependence of elements within complex events.

Likelihood category game model for knowledge consensus

To reach consensus among interacting agents is a problem of interest for social, economical, and political systems. To investigate consensus dynamics, naming game, as a computational and mathematical framework, is commonly used. Existing works mainly focus on the consensus process of vocabulary evolution in a population of agents. However, in real-world cases, naming is not an independent process but relies on perception and categorization. In order to name an object, agents must first distinguish the object according to its features. We thus articulate a likelihood category game model (LCGM) to integrate feature learning and the naming process. In the LCGM, self-organized agents can define category based on acquired knowledge through learning and use likelihood estimation to distinguish objects. The information communicated among the agents is no longer simply in some form of absolute answer, but involves one’s self perception and determination. With its distinguished features, LCGM allows coexistence of multiple categories for an observation. It also provides quantitative explanation that consensus is hard to be reached among serious agents who have a more complex knowledge formation. The proposed LCGM and this study are able to provide new insights into the emergence and evolution of consensus in complex systems.

Reproducibility of aortic valve calcification scoring with computed tomography – An interplatform analysis

BackgroundTo investigate whether aortic valve calcification (AVC) scoring performed with different workstation platforms generates comparable and thus software-independent results. MethodsIn this IRB-approved retrospective study, we included 100 consecutive patients with symptomatic aortic stenosis undergoing CT prior to transcatheter aortic valve implantation. Two independent observers performed AVC scoring on non-enhanced images with commercially available software platforms of four vendors (GE, Philips, Siemens, 3mensio). Gender-specific Agatston score cut-off values were applied according to current recommendations to assign patients to different likelihood categories of aortic stenosis (unlikely to very likely). Comparative analysis of Agatston scores between the four platforms were performed by using Kruskal-Wallis analysis, Spearman rank correlation, linear regression analysis, and Bland-Altman analysis. Differences in category assignment were compared using Fisher's exact test and Cohen's kappa. ResultsFor both observers, each workstation platform produced slightly different numeric AVC Agatston scores, however, without statistical significance (p = 0.96 and p = 0.98). Excellent correlation was found between platforms, with r = 0.991–0.996 (Spearman) and r2 = 0.981–0.992 (regression analysis) for both observers. Bland-Altman analyses revealed small mean differences with narrow limits of agreement between platforms (mean differences: 6 ± 128 to 100 ± 179), for inter-observer (mean differences: 1 ± 43 to 12 ± 70), and intra-observer variability (mean differences: 9 ± 42 to 20 ± 96). Observer 1 assigned 11 (kappa: 0.85–0.97) and observer 2 assigned 10 patients (kappa: 0.88–0.95) to different likelihood groups of severe aortic stenosis with at least one platform. Overall, there was no significant difference of likelihood assignment between platforms (p = 0.98 and p = 1.0, respectively). ConclusionWhile absolute values differ slightly, common commercially available software platforms produce comparable results for AVC scoring, which indicates software-independence of the method.