Multivariable prediction models are used in oral health care to identify individuals with an increased likelihood of caries increment. The outcomes of the models should help to manage individualized interventions and to determine the periodicity of service. The objective was to review and critically appraise studies of multivariable prediction models of caries increment.
Longitudinal studies that developed or validated prediction models of caries and expressed caries increment as a function of at least three predictors were included. PubMed, Cochrane Library, and Web of Science supplemented with reference lists of included studies were searched. Two reviewers independently extracted data using CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) and assessed risk of bias and concern regarding applicability using PROBAST (Prediction model Risk Of Bias ASessment Tool). Predictors were analysed and model performance was recalculated as estimated positive (LR +) and negative likelihood ratios (LR -) based on sensitivity and specificity presented in the studies included.
Among the 765 reports identified, 21 studies providing 66 prediction models fulfilled the inclusion criteria. Over 150 candidate predictors were considered, and 31 predictors remained in studies of final developmental models: caries experience, mutans streptococci in saliva, fluoride supplements, and visible dental plaque being the most common predictors. Predictive performances varied, providing LR + and LR - ranges of 0.78-10.3 and 0.0-1.1, respectively. Only four models of coronal caries and one root caries model scored LR + values of at least 5. All studies were assessed as having high risk of bias, generally due to insufficient number of outcomes in relation to candidate predictors and considerable uncertainty regarding predictor thresholds and measurements. Concern regarding applicability was low overall.
The review calls attention to several methodological deficiencies and the significant heterogeneity observed across the studies ruled out meta-analyses. Flawed or distorted study estimates lead to uncertainty about the prediction, which limits the models\' usefulness in clinical decision-making. The modest performance of most models implies that alternative predictors should be considered, such as bacteria with acid tolerant properties.
PROSPERO CRD#152,467 April 28, 2020.

The human skeleton displays an immense array of traits and variant features that are elements of inter-individual variability. The general assumption is that they may represent individualizing markers for the personal identification of unidentified decedents, but very few works consider them as such. This review provides an overview on the possible use of non-metric traits and skeletal variants for personal identification. The paper discusses the issues related to unquantified comparisons, then it presents a statistical approach based on frequencies of these features for identifying unknown remains. Narrowing down an initial number of 1000 papers, the core of the review is represented by 10 papers that considered non-metric traits and skeletal variants as individualizing features, according to both qualitative and quantitative assessments. Despite visual examination remains the gold-standard, more sound methods are requested to quantify the strength of a match or a mismatch. This especially applies in the wake of juridical demands, hence also satisfying the desire of prosecutors and judges to rely on a \"quantified\" risk. To this purpose, non-metric traits and skeletal variants seem to be a suitable tool to provide quantified evidence, when related frequencies are known.

Pulmonary hypertension (PH) is an insidious disease that often presents in late stages due to nonspecific signs and symptoms. Right heart catheterization (RHC) is the gold standard diagnostic test, and echocardiogram (ECHO) is the best screening tool. However, the strength of evidence and diagnostic utility of various echocardiographic parameters to screen for is not well elucidated. This systematic review (SR) is reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Literature searches was performed for the period of January 1, 2016, to June 1, 2021, on seven databases. We included full-text studies with adult patients that used RHC for comparison and provided sensitivity and specificity results. Likelihood ratios (LRs) and diagnostic odds ratios (DORs) were calculated. Risk of bias was assessed using the Quality Assessment Tool for the Observational Cohort and Cross-Sectional Studies. We identified 102 studies, but only 14 satisfied our inclusion criteria. The most significant parameters identified for PH screening based on LRs are, in descending order, tricuspid regurgitation gradient peak >36mmHg, systolic pulmonary artery pressure >41mmHg, and tricuspid regurgitation velocity >2.9 m/s. There is strong correlation between LR and DOR for these parameters. This SR indicates the superiority of some ECHO parameters over others to aid in the screening and severity assessment of PH. Variables with low LR (-) ratios may help to prevent unnecessary invasive assessment for PH. Clinicians should utilize a multi-parameter approach when interpreting echocardiograms for PH assessment.

Numerous publications focus on fever in returning travellers, but there is no known systematic review considering all diseases, or all tropical diseases causing fever. Such a review is necessary in order to develop appropriate practice guidelines.
Primary objectives of this review were (i) to determine the aetiology of fever in travellers/migrants returning from (sub) tropical countries as well as the proportion of patients with specific diagnoses, and (ii) to assess the predictors for specific tropical diseases.
Embase, MEDLINE and Cochrane Library were searched with terms combining fever and travel/migrants. All studies focusing on causes of fever in returning travellers and/or clinical and laboratory predictors of tropical diseases were included. Meta-analyses were performed on frequencies of etiological diagnoses.
10 064 studies were identified; 541 underwent full-text review; 30 met criteria for data extraction. Tropical infections accounted for 33% of fever diagnoses, with malaria causing 22%, dengue 5% and enteric fever 2%. Non-tropical infections accounted for 36% of febrile cases, with acute gastroenteritis causing 14% and respiratory tract infections 13%. Positive likelihood ratios demonstrated that splenomegaly, thrombocytopenia and hyperbilirubinemia were respectively 5-14, 3-11 and 5-7 times more likely in malaria than non-malaria patients. High variability of results between studies reflects heterogeneity in study design, regions visited, participants\' characteristics, setting, laboratory investigations performed and diseases included.
Malaria accounted for one-fifth of febrile cases, highlighting the importance of rapid malaria testing in febrile returning travellers, followed by other rapid tests for common tropical diseases. High variability between studies highlights the need to harmonize study designs and to promote multi-centre studies investigating predictors of diseases, including of lower incidence, which may help to develop evidence-based guidelines. The use of clinical decision support algorithms by health workers which incorporate clinical predictors, could help standardize studies as well as improve quality of recommendations.

Given the burden of disease and the consequences of a diagnosis of peanut allergy, it is important that peanut allergy be accurately diagnosed so that an appropriate treatment plan can be developed. However, a test that indicates there is peanut sensitization present (eg, a \"positive\" test) is not always associated with clinical reactivity. This practice parameter addresses the diagnosis of IgE-mediated peanut allergy, both in children and adults, as pertaining to 3 fundamental questions, and based on the systematic reviews and meta-analyses, makes recommendations for the clinician who is evaluating a patient for peanut allergy. These questions relate to when diagnostic tests should be completed, which diagnostic tests to utilize, and the utility (or lack thereof) of diagnostic testing to predict the severity of a future allergic reaction to peanut.

DNA analysis has been widely used in the forensic field in order to contribute to identifying the perpetrator of a crime. Forensic investigation in sexual assaults usually focuses on locating and identifying biological fluids, followed by DNA analysis. The identification of certain compounds present in condoms can be useful to reconstruct the occurred event, especially in cases of sexual assaults where the DNA analysis did not show the presence of a male profile and where RNA analysis did not show the presence of sperm markers. Herein we describe the case of a woman reporting to be victim of sexual assault, who was not able to provide accurate information concerning the dynamics of the event; she remembered only forced penile-vaginal penetration by a single perpetrator. We performed short tandem repeat (STR) analyses and mRNA typing for forensic genetics testing on vaginal and rectal swabs collected on the victim, and Fourier-transform infrared spectroscopy (FTIR) followed by chromatographic analyses for the detection of condom compounds on the same swabs. The STR analysis showed only the victim\'s genetic profile, and RNA analysis showed only the presence of vaginal and skin markers. In this situation, the identification of condom compounds residues on vaginal swabs became important as it complemented other collected evidences allowing the Court to reconstruct the events. A proposal of likelihood ratio (LR) calculation for the assessment of the weight of evidence in this case is described.

Forensic scientists and commentators including academics and statisticians have been embroiled in a debate over the best way to present evidence in the courtroom. Various forms of evidence presentation, both quantitative and qualitative, have been championed, yet amidst the furor over the most \"correct\" or \"accurate\" way to present evidence, the perspective of the fact-finder is often lost. Without comprehension, correctness is moot. Unbeknownst to many forensic practitioners, there is a large, though incomplete, body of literature from the cognitive psychology domain that explores the question of what jurors understand when forensic scientists testify. This body of work has begun to test different proposed methods of testimony in an effort to understand which are most effective at communicating the strength of evidence that is intended by the expert. This article is a review of that literature that is intended for the forensic scientist community. Its aim is to educate that community on the findings of completed studies and to identify suggestions for further research that will inform changes in testimony delivery and ensure that any modifications can be implemented with confidence in their effectiveness.

Stiffelman [1] gives a broad critique of the application of likelihood ratios (LRs) in forensic science, in particular their use in probabilistic genotyping (PG) software. These are discussed in this review. LRs do not infringe on the ultimate issue. The Bayesian paradigm clearly separates the role of the scientist from that of the decision makers and distances the scientist from comment on the ultimate and subsidiary issues. LRs do not affect the reasonable doubt standard. Fact finders must still make decisions based on all the evidence and they must do this considering all evidence, not just that given probabilistically. LRs do not infringe on the presumption of innocence. The presumption of innocence does not equate with a prior probability of zero but simply that the person of interest (POI) is no more likely than anyone else to be the donor. Propositions need to be exhaustive within the context of the case. That is, propositions deemed relevant by either defense or prosecution which are not fanciful must not be omitted from consideration.

With recent technological innovations, the multiplication of captured images of criminal events has brought the comparison of faces to the forefront of the judicial scene. Forensic face recognition has become a ubiquitous tool to guide investigations, gather intelligence and provide evidence in court. However, its reliability in court still suffers from the lack of methodological standardization and empirical validation, notably when using automatic systems, which compare images and generate a matching score. Although the use of such systems increases drastically, it still requires more empirical studies based on adequate forensic data (surveillance footage and identity documents) to become a reliable method to present evidence in court. In this paper, we propose a review of the literature leading to the establishment of a methodological workflow to develop a score-based likelihood-ratio computation model using a Bayesian framework. Different approaches are proposed in the literature regarding the within-source and between-sources variability distributions modelling. Depending on the data available, the modelling approach can be specific to the case or generic. Generic approaches allow interpreting the score without any available images of the suspect. Such model is henceforth harder to defend in court because the results are not anchored to the suspect. To make sure the computed score-based LR is robust, we must assess the performance of the model with two main characteristics: the discriminating power and the calibration state of the model. We hence describe the main metrics (Equal Error Rate and Cost of log likelihood-ratio), and graphical representations (Tippett plots, Detection Error Trade-off plot and Empirical Cross-Entropy plot) used to quantify and visualize the performance characteristics.

The evaluation of results of forensic genetic analyses given activity level propositions is an emerging discipline in forensic genetics. Although it is a topic with a long history, it has never been considered to be such a critically important topic for the field, as today. With the increasing sensitivity of analysis techniques, and advances in data interpretation using probabilistic models (\'probabilistic genotyping\'), there is an increasing demand on forensic biologists to share specialised knowledge to help recipients of expert information address mode and timing of transfer and persistence of traces in court. Scientists thereby have a critical role in the assessment of their findings in the context of the case. This helps the judiciary with activity level inferences in a balanced, robust and transparent way, when based on (1) proper case assessment and interpretation respecting the hierarchy of propositions (supported by, for example, the use of Bayesian networks as graphical models), (2) use of appropriate data to inform probabilities, and (3) reporting guidelines by international bodies. This critical review of current literature shows that with certain prerequisites for training and quality assurance, there is a solid foundation for evidence interpretation when propositions of interest are at the \'activity level\'.