We investigated the validity of the International Classification of Diseases 10th revision (ICD-10) (H53.2) diagnostic code for diplopia in the National Ambulatory Care Reporting Systems (NACRS) using a single-centre retrospective chart review study. The “gold-standard” definition was a blinded review of the abstracted chart by a neurology resident physician. Of the included 783 patients, 79 (10.1%) had diplopia as per the gold standard, while 51 (6.5%) had diplopia listed in NACRS. The specificity of the ICD-10 code was 96.9% (95% confidence interval 95.6–98.2), and sensitivity was 36.7% (26.1–47.3). The ICD-10 code for diplopia can reliably identify patients with true diplopia seen in the emergency departments.

Diagnostic test accuracy studies assess a diagnostic test’s performance against a reference standard. In this review, we explore and compare statistical methods used in meta-analyses of diagnostic test accuracy studies. Specifically, we evaluate two frequentist methods – split component synthesis (SCS) and bivariate model (BM) – alongside two Bayesian approaches: Bayesian hierarchical summary receiver operating characteristic (BHSROC) and Bayesian bivariate model (BBM). We also include their latent class variants (LC-BHSROC and LC-BBM). Using a meta-analysis of various multiplex nucleic acid amplification tests (NAATs/PCRs) against Campylobacter spp. as a case study we illustrate the practical applications of these methods. The reference standard was culture, and due to differences in cut-off values and primers among the NAAT/PCR brands, substantial heterogeneity was anticipated. Our findings reveal that the BM and BBM methods tend to estimate higher sensitivities than the other approaches, even when the number of studies is substantial, and heterogeneity is moderate – as observed in this case study. In such scenario, the SCS method or the BHSROC model may offer more robust and reliable outcomes. While our review is based on a real-life meta-analysis rather than simulations, it offers practical insights into the strengths and limitations of these statistical approaches for diagnostic test accuracy studies.

A meta-analysis of diagnostic test accuracy (DTA) studies typically synthesizes study-specific test sensitivity ($Se$) and specificity ($Sp$) to quantify the accuracy of an index test of interest. The bivariate linear mixed effects model with logit transformation of $Se$ and $Sp$ (BLMM-Logit) is commonly used to make statistical inferences, but may lead to misleading results due to the need for Haldane–Anscombe correction and an approximate estimation of variance within the study. Alternative models based on the arcsine square root and Freeman–Tukey double arcsine transformation have been proposed to address these issues; however, they still rely on approximate variance estimation, which is suitable only for large sample sizes. The bivariate generalized linear mixed effects model (BGLMM) is another option, but it faces convergence issues with small meta-analyses or sparse primary studies. To address these limitations, we proposed an exact within-study variance calculation method that does not require Haldane–Anscombe correction and is applicable regardless of the transformation used or the number of studies and participants. We evaluated this method against existing approaches using real-life and simulated DTA meta-analyses. The methods were comparable for large meta-analyses. However, BLMM-Logit demonstrated substantial negative bias in estimating variances between studies and consistently underestimated summary $Se$ and $Sp$ in all simulation scenarios. In contrast, the proposed exact methods (Exact-Logit, Exact-ASR, and Exact-FTDA) and BGLMM had minimal bias and better performance metrics, particularly for meta-analyses with sparse primary studies. Thus, the proposed exact methods should be preferred for DTA meta-analyses with small or sparse studies.

The Global Leadership Initiative on Malnutrition (GLIM) provides a consensus-based diagnostic framework for malnutrition in hospitalised patients, which includes at least one phenotypic and one aetiologic criterion. In GLIM, appendicular skeletal muscle based on bioelectrical impedance analysis (ASMBIA) and calf circumference (CC) are two common techniques for muscle mass assessment, but their accuracy remains debated. Therefore, the present study evaluates the prevalence of malnutrition upon hospital admission applied by GLIM criteria and mainly compares the effectiveness of ASMBIA and CC. We screened a total of 605 patients from four hospitals in Indonesia (August–October 2024). Multivariate logistic regression analysed associations with clinical outcomes. Prevalence of malnutrition was 72·7 % using three phenotypes, 55·9 % with two phenotypes, 22·1 % via ASMBIA and 62·6 % using CC. Significant associations (P < 0·05) were found between malnutrition and weight loss, BMI, mid-upper arm circumference, handgrip strength, sarcopenia and fat-free mass index. For all criteria combinations, sensitivity was greater in CC (86·1 %), followed by two phenotypes (76·8 %), while the ASMBIA had the poorest sensitivity (30·5 %). All GLIM-based diagnostic methods correlated with malnutrition risk screening and nutrition status indicators. The GLIM criteria provide a standardised, clinically relevant approach for diagnosing malnutrition in hospitalised patients, with CC emerging as a highly sensitive assessment to examine muscle mass.

Hippoboscoidea flies exhibit highly specific ectoparasitic relationships with bats, shaped by both intrinsic factors (e.g. bat behaviour) and extrinsic factors (e.g. land use). Understanding the dynamics of these parasite–host interactions is essential for uncovering co-evolutionary patterns and informing conservation strategies. To this end, we studied bat–fly interactions across different elevations in a montane forest of Amazonas, northern Peru. The most abundant bats were Carollia brevicauda, C. perspicillata and Sturnira oporaphilum, while Paraeuctenodes similis and Trichobius joblingi were the most common flies. Most flies exhibited monoxenous host specificity. Bat–fly interaction networks revealed high modularity and specialization at both local and regional scales. Modules typically grouped bat species of the same genus or subfamily, suggesting that phylogenetic constraints and roosting behaviour may shape those interaction patterns. Nestedness within modules (compound structure) emerged in the aggregated regional network, aligning with the integrative hypothesis of specialization. Although network structures were broadly similar across sites, species turnover contributed to subtle differences in module composition and specialization. These differences were congruent with the changes in species roles of certain bats and flies. This study represents the first of its kind in Peru and addresses significant knowledge gaps in the ecology of bat–fly interactions in the Neotropics.

Research has demonstrated that emotion modulates specificity in recollection of personally experienced events and the words individuals use during recollection reflect their psychological states. Here, we investigated the linguistic features of autobiographical memory (AM) of different specificity for different emotional events to address how emotion would modulate the psychological mechanisms underlying AM of different specificity. We analyzed 122 participants’ narratives of AM categorized as specific and general under happy, sad, angry, fearful and neutral cues. The use of three groups (emotional process, cognitive process and thinking style) of words was, respectively, compared between specific and general AM in each emotion condition. In retrieval of sad, angry and fearful events, general relative to specific AM contained more affective process words, notably negative words. General AM featured more cognitive process words than specific AM, regardless of emotion type (except neutral). When recalling happy events, general AM featured more analytic thinking words than specific AM, while in recollection of fearful events, general AM featured fewer such words than specific AM. General relative to specific AM about happy experiences contained more narrative thinking words. These findings suggest that the psychological mechanisms underlying top-down and bottom-up retrieval differ between particular types of emotion engaged in AM.

This study examined the capacity of ChatGPT-4 to assess L2 writing in an accurate, specific, and relevant way. Based on 35 argumentative essays written by upper-intermediate L2 writers in higher education, we evaluated ChatGPT-4’s assessment capacity across four L2 writing dimensions: (1) Task Response, (2) Coherence and Cohesion, (3) Lexical Resource, and (4) Grammatical Range and Accuracy. The main findings were (a) ChatGPT-4 was exceptionally accurate in identifying the issues across the four dimensions; (b) ChatGPT-4 demonstrated more variability in feedback specificity, with more specific feedback in Grammatical Range and Accuracy and Lexical Resource, but more general feedback in Task Response and Coherence and Cohesion; and (c) ChatGPT-4’s feedback was highly relevant to the criteria in the Task Response and Coherence and Cohesion dimensions, but it occasionally misclassified errors in the Grammatical Range and Accuracy and Lexical Resource dimensions. Our findings contribute to a better understanding of ChatGPT-4 as an assessment tool, informing future research and practical applications in L2 writing assessment.

This study aims to illustrate a process approach for the calculation of minimum dietary diversity (MDD) indicators for interpretation of dietary diversity (DD) scores and to validate the MDD indicator as a proxy for adequate micronutrient intake using an existing dataset for 2 to younger than 10-year-old South African children. The DD scores were derived from nine food groups, adjusted from the ten food groups for women of reproductive age by combining pulses, nuts and seeds. Three reference methods were used to inspect micronutrient adequacy, namely the mean adequacy ratio and the mean probability of adequacy (MPA) using a single 24-h recall, and the MPA derived from usual intake using more than one 24-hour recall in a sub-sample. Adequacy threshold levels and candidate MDD indicators were inspected and validated using several performance criteria. Results show that the mean and median DD scores were 3·6 and 3·1, respectively. The resulting MDD indicators varied between 3 and 4 out of nine food groups favouring the identification of children with adequate and inadequate intake, respectively, depending on the method used and the age group. Our results and those from others furthermore support a simplified method or ‘rule of thumb’ for the determination of an MDD indicator to establish the integer values below and above the median of the DD scores. We conclude that finding a valid MDD indicator can be done using different methodologies and that results underscore the potential of a simplified method for determining an MDD indicator.

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.