Meta-analysis of diagnostic studies experience the common problem that different studies might not be comparable since they have been using a different cut-off value for the continuous or ordered categorical diagnostic test value defining different regions for which the diagnostic test is defined to be positive. Hence specificities and sensitivities arising from different studies might vary just because the underlying cut-off value had been different. To cope with the cut-off value problem interest is usually directed towards the receiver operating characteristic (ROC) curve which consists of pairs of sensitivities and false-positive rates (1-specificity). In the context of meta-analysis one pair represents one study and the associated diagram is called an SROC curve where the S stands for “summary”. In meta-analysis of diagnostic studies emphasis has traditionally been placed on modelling this SROC curve with the intention of providing a summary measure of the diagnostic accuracy by means of an estimate of the summary ROC curve. Here, we focus instead on finding sub-groups or components in the data representing different diagnostic accuracies. The paper will consider modelling SROC curves with the Lehmann family which is characterised by one parameter only. Each single study can be represented by a specific value of that parameter. Hence we focus on the distribution of these parameter estimates and suggest modelling a potential heterogeneous or cluster structure by a mixture of specifically parameterised normal densities. We point out that this mixture is completely nonparametric and the associated mixture likelihood is well-defined and globally bounded. We use the theory and algorithms of nonparametric mixture likelihood estimation to identify a potential cluster structure in the diagnostic accuracies of the collection of studies to be analysed. Several meta-analytic applications on diagnostic studies, including AUDIT and AUDIT-C for detection of unhealthy alcohol use, the mini-mental state examination for cognitive disorders, as well as diagnostic accuracy inspection data on metal fatigue of aircraft spare parts, are discussed to illustrate the methodology.

The present paper investigates whether school-aged French-English bilingual children’s implicit and explicit knowledge of article use is affected by cross-linguistic influence (CLI) during online and offline sentence comprehension. The studies focus on the encoding of plural and mass nouns in specific and generic contexts. We also explore whether individual measures of oral proficiency, language exposure and age play a role in the children’s performance. Forty-three 8-to-10-year-old French-English bilingual children took part in a Self-Paced Reading task, a Grammaticality Judgement task and a Cloze test in their two languages. Overall, CLI was observed across tasks in English and French. These findings suggest that CLI can be bi-directional and tap into school-aged bilinguals’ implicit and explicit representations during sentence comprehension and production. The data also makes a new contribution to our understanding of the relative amount of language exposure, oral proficiency and age on CLI.

Malnutrition significantly hampers wound healing processes. This study aimed to compare the effectiveness of the Global Leadership Initiative on Malnutrition (GLIM) and Subjective Global Assessment (SGA) in diagnosing malnutrition and predicting wound healing in patients with diabetic foot ulcers (DFU). GLIM criteria were evaluated for sensitivity (SE), specificity (SP), positive predictive value, negative predictive value and kappa (κ) against SGA as the reference. Modified Poisson regression model and the DeLong test investigated the association between malnutrition and non-healing ulcers over 6 months. This retrospective cohort study included 398 patients with DFU, with a mean age of 66·3 ± 11·9 years. According to SGA and GLIM criteria, malnutrition rates were 50·8 % and 42·7 %, respectively. GLIM criteria showed a SE of 67·3 % (95 % CI 60·4 %, 73·7 %) and SP of 82·7 % (95 % CI 76·6 %, 87·7 %) in identifying malnutrition, with a positive predictive value of 80·0 % and a negative predictive value of 71·1 % (κ = 0·50) compared with SGA. Multivariate analysis demonstrated that malnutrition, as assessed by SGA, was an independent risk factor for non-healing (relative risk (RR) 1·84, 95 % CI 1·45, 2·34), whereas GLIM criteria were associated with poorer ulcer healing in patients with estimated glomerular filtration rate ≥ 60 ml/min/1·73m2 (RR: 1·46, 95 % CI 1·10, 1·94). SGA demonstrated a superior area under the receiver’s operating characteristic curve for predicting non-healing compared with GLIM criteria (0·70 (0·65–0·75) v. 0·63 (0·58–0·65), P < 0·01). These findings suggest that both nutritional assessment tools effectively identify patients with DFU at increased risk, with SGA showing superior performance in predicting non-healing ulcers.

Abstraction processes involve two variables that are often confused with one another: concreteness (banana versus belief) and specificity (chair versus furniture or Buddhism versus religion). Researchers are investigating the relationship between them, but many questions remain open, such as: What type of semantics characterizes words with varying degrees of concreteness and specificity? We tackle this topic through an in-depth semantic analysis of 1049 Italian words for which human-generated concreteness and specificity ratings are available. Our findings show that (as expected) the semantics of concrete and abstract concepts differs, but most interestingly when specificity is considered, the variance in concreteness ratings explained by semantic types increases substantially, suggesting the need to carefully control word specificity in future research. For instance, mathematical concepts (phase) are on average abstract and generic, while behavioral qualities (arrogant) are on average abstract but specific. Moreover, through cluster analyses based on concreteness and specificity ratings, we observe the bottom-up emergence of four subgroups of semantically coherent words. Overall, this study provides empirical evidence and theoretical insight into the interplay of concreteness and specificity in shaping semantic categorization.

Incomplete original descriptions, the unavailability or poor conditions of specimens and the lack of detailed redescriptions have caused the validity of several species of the genus Encotyllabe Diesing, 1850 to be questioned. To date, seven of the recognized species were described upon one or two specimens, hindering study of intraspecific variations. This was made worse by considering few morphoanatomical differences sufficient to erect new species. Among Encotyllabe spp. occurring in Mediterranean waters, E. vallei was first described from the gilt-head bream Sparus aurata (Sparidae) off Italy. Although beautifully illustrated for a paper from that century, morphometric data for E. vallei from the type-host S. aurata remain unavailable. Previous records of E. vallei provided either morphometrical or molecular data, and its validity was questioned. We provide a redescription of E. vallei based on newly collected specimens from the S. aurata from the southwestern Mediterranean (off Algeria) using integrative taxonomy. Analysis of cox1 sequences of E. vallei from S. aurata, compared to sequences from other sparid hosts, mainly Pagellus bogaraveo, revealed a divergence not exceeding 2%, suggesting a stenoxenic specificity for this monogenean. Given that P. bogaraveo is the type-host for Encotyllabe pagelli, we were tempted to suggest a synonymy between E. vallei and E. pagelli. We refrained from doing so because E. pagelli was first described from the Atlantic coast off Brest, France. Morphological data for Encotyllabe from P. bogaraveo are warranted assessing the host specificity of E. vallei and whether there might be a species complex within individual sparid fish species.

Identifying nutritional deficits and implementing appropriate interventions in patients requiring vascular surgery is challenging due to the paucity of appropriate screening and assessment tools in this group. This retrospective study aimed to determine the validity of the Global Leadership Initiative on Malnutrition (GLIM) in identifying protein–energy malnutrition (PEM) in inpatients admitted to a vascular surgery unit, using the PG-SGA as the comparator. Diagnostic accuracy and consistency were determined between the GLIM and the Patient-Generated Subjective Global Assessment (PG-SGA) global rating. The GLIM determination was made retrospectively using the relevant parameters collected at baseline in the original study. Two hundred and twenty-four (70·1 % male) participants were included. The prevalence of PEM was 28·6 % on GLIM and 17 % via the PG-SGA. Compared with the PG-SGA, the GLIM achieved sensitivity of 73·7 % and specificity of 80·6 %; however positive predictive value was 43·7 % indicating that the GLIM over-diagnosed malnutrition compared with the PG-SGA. Kappa reached 0·427 indicating moderate diagnostic consistency. Due to the absence of an ideal instrument and the complexity of malnutrition often seen in this group which extends beyond PEM to significant micronutrient deficiencies, further work is required to determine the most appropriate instrument in this patient group, and how micronutrient status can also be included in the overall assessment given the critical role of micronutrients in this group.

Whole-genome sequencing (WGS) has shown tremendous potential in rapid diagnosis of drug-resistant tuberculosis (TB). In the current study, we performed WGS on drug-resistant Mycobacterium tuberculosis isolates obtained from Shanghai (n = 137) and Russia (n = 78). We aimed to characterise the underlying and high-frequency novel drug-resistance-conferring mutations, and also create valuable combinations of resistance mutations with high predictive sensitivity to predict multidrug- and extensively drug-resistant tuberculosis (MDR/XDR-TB) phenotype using a bootstrap method. Most strains belonged to L2.2, L4.2, L4.4, L4.5 and L4.8 lineages. We found that WGS could predict 82.07% of phenotypically drug-resistant domestic strains. The prediction sensitivity for rifampicin (RIF), isoniazid (INH), ethambutol (EMB), streptomycin (STR), ofloxacin (OFL), amikacin (AMK) and capreomycin (CAP) was 79.71%, 86.30%, 76.47%, 88.37%, 83.33%, 70.00% and 70.00%, respectively. The mutation combination with the highest sensitivity for MDR prediction was rpoB S450L + rpoB H445A/P + katG S315T + inhA I21T + inhA S94A, with a sensitivity of 92.17% (0.8615, 0.9646), and the mutation combination with highest sensitivity for XDR prediction was rpoB S450L + katG S315T + gyrA D94G + rrs A1401G, with a sensitivity of 92.86% (0.8158, 0.9796). The molecular information presented here will be of particular value for the rapid clinical detection of MDR- and XDR-TB isolates through laboratory diagnosis.

In this paper we present an original approach to analyze the compositionality of indefinite expressions in Romance by investigating the relevance of their syntactic distribution in relation to their meaning. This approach has the advantage of allowing us to explore the question of how syntactic structure can determine the meaning of different forms of indefiniteness. To that end, we postulate a common derivation for bare plurals, bare mass and de phrases, whereby an abstract operator de is adjoined to definite determiners and shifts entities into property-type expressions. Quantificational specificity is proposed to be derived from a syntactic structure in which weak quantifiers select for indefinite de-phrases, no matter whether de is overt at Spell-Out or not; these quantifiers turn properties into generalized quantifiers. The anti-specificity meaning of some indefinites is derived by adjoining in the syntactic structure an abstract operator alg that encodes the speaker’s epistemic state of ignorance to a quantifier encoded for specificity, and it turns a generalized quantifier into a modified generalized quantifier. The paper also brings some general predictions on how indefiniteness is expressed in Romance, as it provides extensive support from five Romance languages: Brazilian Portuguese, Catalan, French, Italian and Spanish.

The test-negative design (TND) has become a standard approach for vaccine effectiveness (VE) studies. However, previous studies suggested that it may be more vulnerable than other designs to misclassification of disease outcome caused by imperfect diagnostic tests. This could be a particular limitation in VE studies where simple tests (e.g. rapid influenza diagnostic tests) are used for logistical convenience. To address this issue, we derived a mathematical representation of the TND with imperfect tests, then developed a bias correction framework for possible misclassification. TND studies usually include multiple covariates other than vaccine history to adjust for potential confounders; our methods can also address multivariate analyses and be easily coupled with existing estimation tools. We validated the performance of these methods using simulations of common scenarios for vaccine efficacy and were able to obtain unbiased estimates in a variety of parameter settings.