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The bipartite record linkage task consists of merging two disparate datafiles containing information on two overlapping sets of entities. This is nontrivial in the absence of unique identifiers and it is important for a wide variety of applications given that it needs to be solved whenever we have to combine information from different sources. Most statistical techniques currently used for record linkage are derived from a seminal article by Fellegi and Sunter in 1969 . These techniques usually assume independence in the matching statuses of record pairs to derive estimation procedures and optimal point estimators. We argue that this independence assumption is unreasonable and instead target a bipartite matching between the two datafiles as our parameter of interest. Bayesian implementations allow us to quantify uncertainty on the matching decisions and derive a variety of point estimators using different loss functions. We propose partial Bayes estimates that allow uncertain parts of the bipartite matching to be left unresolved. We evaluate our approach to record linkage using a variety of challenging scenarios and show that it outperforms the traditional methodology. We illustrate the advantages of our methods merging two datafiles on casualties from the civil war of El Salvador. Supplementary materials for this article are available online.

Clinical judgement in primary care is more often decisive than in the hospital. Clinical decision rules (CDRs) can help general practitioners facilitating the work-through of differentials that follows an initial suspicion, resulting in a concrete ‘course of action’: a ‘rule-out’ without further testing, a need for further testing, or a specific treatment. However, in daily primary care, the use of CDRs is limited to only a few isolated rules. In this paper, we aimed to provide insight into the laborious path required to implement a viable CDR. At the same time, we noted that the limited use of CDRs in primary care cannot be explained by implementation barriers alone. Through the case study of the Oudega rule for the exclusion of deep vein thrombosis, we concluded that primary care CDRs come out best if they are tailor-made, taking into consideration the specific context of primary health care. Current CDRs should be evaluated frequently, and future decision rules should anticipate the latest developments such as the use of point-of-care (POC) tests. Hence, such new powerful diagnostic CDRs could improve and expand the possibilities for patient-oriented primary care.

We provide an exact nonasymptotic lower bound on the minimax expected excess risk (EER) in the agnostic probably-approximately-correct (PAC) machine learning classification model and identify minimax learning algorithms as certain maximally symmetric and minimally randomized “voting” procedures. Based on this result, an exact asymptotic lower bound on the minimax EER is provided. This bound is of the simple form c ∞/√ν → ∞, where c ∞ = 0.16997 . . . is a universal constant, ν = m/d, m is the size of the training sample and d is the Vapnik–Chervonenkis dimension of the hypothesis class. It is shown that the differences between these asymptotic and nonasymptotic bounds, as well as the differences between these two bounds and the maximum EER of any learning algorithms that minimize the empirical risk, are asymptotically negligible, and all these differences are due to ties in the mentioned “voting” procedures. A few easy to compute nonasymptotic lower bounds on the minimax EER are also obtained, which are shown to be close to the exact asymptotic lower bound c ∞/√ν even for rather small values of the ratio ν = m/d. As an application of these results, we substantially improve existing lower bounds on the tail probability of the excess risk. Among the tools used are Bayes estimation and apparently new identities and inequalities for binomial distributions.

The aim of the study was to develop an improved search strategy for clinical prediction rules. We first refined a list of 30 primary care–relevant journals and improved the efficiency of the Haynes Narrow Filter/Teljour/Murphy Inclusion Filter with 26 items by removing one term (Modified Haynes 26 filter). We then developed the “Royal College of Surgeons in Ireland (RCSI) filter” and compared it with the modified HNF/TMIF26 for its ability to detect prediction rules in the primary care literature. All abstracts and, if necessary, full text were reviewed independently in parallel by primary care physicians. The key outcomes were the percentage of prediction rules identified out of the total identified by both search strategies (sensitivity) and the number of articles that had to be reviewed to identify them (efficiency). The Modified Haynes 26 filter returned 1,701 abstracts vs. 1,062 for the RCSI filter. The RCSI filter identified 105 of 111 of all prediction rules identified by either filter, compared with 107 of 111 by the Modified Haynes 26 filter (94.6% vs. 96.4%; P = 0.52). In addition, 9.9% of abstracts found using the RCSI filter were prediction rules, compared with only 6.3% using the Modified Haynes 25 filter (P = 0.001). We have developed a novel “RCSI filter” that more efficiently identifies prediction rules in the medical literature.

IntroductionThe aim of this study was to develop and validate an easy to use clinical decision rule, applicable in the ED that limits the number of unnecessary cast immobilizations and diagnostic follow-up in suspected scaphoid injury, without increasing the risk of missing fractures.MethodsA prospective multicenter study was conducted that consisted of three components: (1) derivation of a clinical prediction model for detecting scaphoid fractures in adult patients following wrist trauma; (2) internal validation of the model; (3) design of a clinical decision rule. The predictors used were: sex, age, swelling of the anatomic snuffbox, tenderness in the anatomic snuffbox, scaphoid tubercle tenderness, painful ulnar deviation and painful axial thumb compression. The outcome measure was the presence of a scaphoid fracture, diagnosed on either initial radiographs or during re-evaluation after 1–2 weeks or on additional imaging (radiographs/MRI/CT). After multivariate logistic regression analysis and bootstrapping, the regression coefficient for each significant predictor was calculated. The effect of the rule was determined by calculating the number of missed scaphoid fractures and reduction of suspected fractures that required a cast.ResultsA consecutive series of 893 patients with acute wrist injury was included. Sixty-eight patients (7.6%) were diagnosed with a scaphoid fracture. The final prediction rule incorporated sex, swelling of the anatomic snuffbox, tenderness in the anatomic snuffbox, painful ulnar deviation and painful axial thumb compression. Internal validation of the prediction rule showed a sensitivity of 97% and a specificity of 20%. Using this rule, a 15% reduction in unnecessary immobilization and imaging could be achieved with a 50% decreased risk of missing a fracture compared with current clinical practice.ConclusionsThis dataset provided a simple clinical decision rule for scaphoid fractures following acute wrist injury that limits unnecessary immobilization and imaging with a decreased risk of missing a fracture compared to current clinical practice.Clinical prediction rule1/(1 + EXP (−(0.649662618 × if man) + (0.51353467826 × if swelling anatomic snuffbox) + (−0.79038263985 × if painful palpation anatomic snuffbox) + (0.57681198857 × if painful ulnar deviation) + (0.66499549728 × if painful thumb compression)−1.685).Trial registrationTrial register NTR 2544, www.trialregister.nl.

Background Gastroenteritis (GE) is a non-specific term for various pathologic states of the gastrointestinal tract. Infectious agents usually cause acute GE. At present, there are no robust decision-making rules that predict bacterial GE and dictate when to start antibiotics for patients suffering from acute GE to the emergency department (ED). We aim to define a clinical prediction rule to aid in the diagnosis of bacterial GE, requiring empirical antibiotics in adult patients presenting to the emergency department with acute GE. Methods A two-year retrospective case review was performed on all cases from July 2015 to June 2017 that included patients with acute GE symptoms referred to the ED, after which their stool cultures were performed. The clinical parameters analyzed included patient with comorbid conditions, physical examination findings, historical markers, point-of-care and radiographic tests and other laboratory work. We then used multi-variate logistic regression analysis on each group (bacterial culture–positive GE and bacterial culture–negative GE) to elucidate clinical criteria with the highest yield for predicting bacterial gastroenteritis (BGE). Results A total of 756 patients with a mean age of 52 years, 52% female and 48% male, respectively, were included in the study. On the basis of the data of these patients, we suggested using a scoring system to delineate the need for empirical antibiotics in patients with suspected bacterial GE based on six clinical and laboratory variables. We termed this the BGE score. A score 0 – 2 points suggests low risk (0.9%) of bacterial GE. A score of 3 – 4 points confers an intermediate risk of 12.0% and a score of 5 – 8 points confers a high risk of 85.7%. A cut-off of  ≥ 5 points may be used to predict culture-positive BGE with a 75% sensitivity and 75% specificity. The area under the receiver operating characteristic (AUROC) for the scoring system (range 0 – 8) was 0.812 (95% CI: 0.780–0.843) p -value < 0.001. Conclusion We suggest using the BGE scoring system (cut-off ≥ 5 points) to delineate the need for empirical antibiotics in patients diagnosed with gastroenteritis. While this is a pilot study, which will require further validation with a larger sample size, our proposed decision-making rule will potentially serve to improve the diagnosis of BGE and thus reduce unnecessary prescription of antibiotics, which will in turn reduce antibiotic-associated adverse events and save on costs worldwide.

The Pediatric Emergency Care Applied Research Network (PECARN) developed a clinical decision rule to identify children at low risk for intra-abdominal injury requiring acute intervention (IAI-I) for reducing unnecessary radiation exposure of ab-dominal computed tomography (CT) after blunt torso trauma. This study aimed to compare the PECARN decision rule with clinician suspicion in identifying children at low risk of intra-abdominal injuries that an abdominal CT scan can be safely avoided. This study is a retrospective review of children with blunt torso trauma in an academic emergency department (ED) between 2011 and 2019. Patients were considered positive for the PECARN rule if they exhibited any of the variables. Clinician suspi-cion was defined as actual CT ordering of the treating physician. The primary outcome was IAI-I detected by imaging or surgery within 1 month after the trauma, and the secondary outcome was any intra-abdominal injury (IAI) presence. Among the 768 children included, 48 (6.25%) had intra-abdominal injuries and 21 (2.73%) of whom underwent acute in-tervention. Four hundred and fifty-three (59%) children underwent abdominal CT scanning. If the PECARN rule had been applied, 232 patients would have undergone abdominal CT. The rule revealed 90.48% (95% CI=68.17-98.33%) sensitivity for IAI-I and 81.25% (95% CI=66.9-90.56%) for IAI. Clinician suspicion revealed sensitivities of 100% (95% CI=80.76-00%) and 93.75% (95% CI=81.79-98.37%) for IAI-I and IAI, respectively. Sensitivities of the rule and clinician suspicion were statistically similar for both IAI-I (p=0.5) and IAI (p=0.146). In this study, the PECARN abdominal rule and clinician suspicion performed similarly in identifying intra-abdominal injuries in children with blunt torso trauma. However, our study supports the use of PECARN abdominal rule in addition to clinical judgment to limit unnecessary abdominal CT use in pediatric patients with blunt torso trauma in the ED.

Accurate and reliable recognition of fundamental heart sounds (FHSs) plays a significant role in automated analysis of heart sound (HS) patterns. This Letter presents an automated wavelet-based FHS recognition (WFHSR) method using morphological and interval features. The proposed method first performs the decomposition of phonocardiogram (PCG) signal using a synchrosqueezing wavelet transform to extract the HSs and suppresses the murmurs, low-frequency and high-frequency noises. The HS delineation (HSD) is presented using Shannnon energy envelope and amplitude-dependent thresholding rule. The FHS recognition (FHSR) is presented using interval, HS duration and envelope area features with a decision-rule algorithm. The performance of the method is evaluated on PASCAL HSs Challenge, PhysioNet/CinC HS Challenge, eGeneralMedical databases and real-time recorded PCG signals. Results show that the HSD approach achieves an average sensitivity (Se) of 98.87%, positive predictivity (Pp) of 97.50% with detection error rate of 3.67% for PCG signals with signal-to-noise ratio of 10 dB, and outperforms the existing HSD methods. The proposed FHSR method achieves a Se of 99.00%, Sp of 99.08% and overall accuracy of 99.04% on both normal and abnormal PCG signals. Evaluation results show that the proposed WFHSR method is able to accurately recognise the S1/S2 HSs in noisy real-world PCG recordings with murmurs and other abnormal sounds.

Spectrum sensing in a cognitive radio network involves detecting when a primary user vacates their licensed spectrum, to enable secondary users to broadcast on the same band. Accurately sensing the absence of the primary user ensures maximum utilization of the licensed spectrum and is fundamental to building effective cognitive radio networks. In this paper, we address the issues of enhancing sensing gain, average throughput, energy consumption, and network lifetime in a cognitive radio-based Internet of things (CR-IoT) network using the non-sequential approach. As a solution, we propose a Dempster–Shafer theory-based throughput analysis of an energy-efficient spectrum sensing scheme for a heterogeneous CR-IoT network using the sequential approach, which utilizes firstly the signal-to-noise ratio (SNR) to evaluate the degree of reliability and secondly the time slot of reporting to merge as a flexible time slot of sensing to more efficiently assess spectrum sensing. Before a global decision is made on the basis of both the soft decision fusion rule like the Dempster–Shafer theory and hard decision fusion rule like the “n-out-of-k” rule at the fusion center, a flexible time slot of sensing is added to adjust its measuring result. Using the proposed Dempster–Shafer theory, evidence is aggregated during the time slot of reporting and then a global decision is made at the fusion center. In addition, the throughput of the proposed scheme using the sequential approach is analyzed based on both the soft decision fusion rule and hard decision fusion rule. Simulation results indicate that the new approach improves primary user sensing accuracy by 13% over previous approaches, while concurrently increasing detection probability and decreasing false alarm probability. It also improves overall throughput, reduces energy consumption, prolongs expected lifetime, and reduces global error probability compared to the previous approaches under any condition [part of this paper was presented at the EuCAP2018 conference (Md. Sipon Miah et al. 2018)].

A series of experiments demonstrates that consumers exhibit aversion to waste during forward-looking purchase. These experiments further reveal that such behavior is driven by distaste for unused utility, a reaction that is shown to be distinct from an aversion to squandering money. Waste aversion is especially pronounced when consumers anticipate future consequences and deprivation is salient. In addition to demonstrating robustness across consumers and marketing contexts, the results also demonstrate how waste aversion can lead to self-defeating behavior in which consumers forego desired utility. Finally, the present research demonstrates and discusses the implications of waste aversion for a variety of marketing issues, including buy-rent markets, bundling, and the fundamental distinction between goods and services.