Shortcut citations in the methods section: Frequency, problems, and strategies for responsible reuse.

Methods sections are often missing essential details. Methodological shortcut citations, in which authors cite previous papers instead of describing the method in detail, may contribute to this problem. This meta-research study used 3 approaches to examine shortcut citation use in neuroscience, biology, and psychiatry. First, we assessed current practices in more than 750 papers. More than 90% of papers used shortcut citations. Other common reasons for using citations in the methods included giving credit or specifying what was used (who or what citation) and providing context or a justification (why citation). Next, we reviewed 15 papers to determine what can happen when readers follow shortcut citations to find methodological details. While shortcut citations can be used effectively, they can also deprive readers of essential methodological details. Problems encountered included difficulty identifying or accessing the cited materials, missing or insufficient descriptions of the cited method, and shortcut citation chains. Third, we examined journal policies. Fewer than one quarter of journals had policies describing how authors should report previously described methods. We propose that methodological shortcut citations should meet 3 criteria; cited resources should provide (1) a detailed description of (2) the method used by the citing authors', and (3) be open access. Resources that do not meet these criteria should be cited to give credit, but not as shortcut citations. We outline actions that authors and journals can take to use shortcut citations responsibly, while fostering a culture of open and reproducible methods reporting.

Machine learning using multimodal and autonomic nervous system parameters predicts clinically apparent stroke-associated pneumonia in a development and testing study.

BACKGROUND: Stroke-associated pneumonia (SAP) is a preventable determinant for poor outcome after stroke. Machine learning (ML) using large-scale clinical data warehouses may be able to predict SAP and identify patients for targeted interventions. The aim of this study was to develop a prediction model for identifying clinically apparent SAP using automated ML. METHODS: The ML model used clinical and laboratory parameters along with heart rate (HR), heart rate variability (HRV), and blood pressure (BP) values obtained during the first 48 h after stroke unit admission. A logistic regression classifier was developed and internally validated with a nested-cross-validation (nCV) approach. For every shuffle, the model was first trained and validated with a fixed threshold for 0.9 sensitivity, then finally tested on the out-of-sample data and benchmarked against a widely validated clinical score (A2DS2). RESULTS: We identified 2390 eligible patients admitted to two-stroke units at Charité between October 2020 and June 2023, of whom 1755 had all parameters available. SAP was diagnosed in 96/1755 (5.5%). Circadian profiles in HR, HRV, and BP metrics during the first 48 h after admission exhibited distinct differences between patients with SAP diagnosis vs. those without. CRP, mRS at admission, leukocyte count, high-frequency power in HRV, stroke severity at admission, sex, and diastolic BP were identified as the most informative ML features. We obtained an AUC of 0.91 (CI 0.88-0.95) for the ML model on the out-of-sample data in comparison to an AUC of 0.84 (CI 0.76-0.91) for the previously established A2DS2 score (p < 0.001). The ML model provided a sensitivity of 0.87 (CI 0.75-0.97) with a corresponding specificity of 0.82 (CI 0.78-0.85) which outperformed the A2DS2 score for multiple cutoffs. CONCLUSIONS: Automated, data warehouse-based prediction of clinically apparent SAP in the stroke unit setting is feasible, benefits from the inclusion of vital signs, and could be useful for identifying high-risk patients or prophylactic pneumonia management in clinical routine.

Fetal Origins of Mental Disorders? An Answer Based on Mendelian Randomization.

The Barker hypothesis states that low birth weight (BW) is associated with higher risk of adult onset diseases, including mental disorders like schizophrenia, major depressive disorder (MDD), and attention deficit hyperactivity disorder (ADHD). The main criticism of this hypothesis is that evidence for it comes from observational studies. Specifically, observational evidence does not suffice for inferring causality, because the associations might reflect the effects of confounders. Mendelian randomization (MR) - a novel method that tests causality on the basis of genetic data - creates the unprecedented opportunity to probe the causality in the association between BW and mental disorders in observation studies. We used MR and summary statistics from recent large genome-wide association studies to test whether the association between BW and MDD, schizophrenia and ADHD is causal. We employed the inverse variance weighted (IVW) method in conjunction with several other approaches that are robust to possible assumption violations. MR-Egger was used to rule out horizontal pleiotropy. IVW showed that the association between BW and MDD, schizophrenia and ADHD is not causal (all p > .05). The results of all the other MR methods were similar and highly consistent. MR-Egger provided no evidence for pleiotropic effects biasing the estimates of the effects of BW on MDD (intercept = -0.004, SE = 0.005, p = .372), schizophrenia (intercept = 0.003, SE = 0.01, p = .769), or ADHD (intercept = 0.009, SE = 0.01, p = .357). Based on the current evidence, we refute the Barker hypothesis concerning the fetal origins of adult mental disorders. The discrepancy between our results and the results from observational studies may be explained by the effects of confounders in the observational studies, or by the existence of a small causal effect not detected in our study due to weak instruments. Our power analyses suggested that the upper bound for a potential causal effect of BW on mental disorders would likely not exceed an odds ratio of 1.2.