Studies of the superior longitudinal fasciculus (SLF) have multiplied in recent decades owing to methodological advances, but the absence of a convention for nomenclature remains a source of confusion. Here, we have reviewed existing nomenclatures in the context of the research studies that generated them and we have identified their agreements and disagreements. A literature search was conducted using PubMed/MEDLINE, Web‐of‐Science, Embase, and a review of seminal publications, without restrictions regarding publication date. Our search revealed that diffusion imaging, autoradiography, and fiber dissection have been the main methods contributing to tract designation. The first two have been particularly influential in systematizing the horizontal elements distant from the lateral sulcus. Twelve approaches to naming were identified, eight of them differing considerably from each other. The terms SLF and arcuate fasciculus (AF) were often used as synonyms until the second half of the 20th century. During the last 15 years, this has ceased to be the case in a growing number of publications. The term AF has been used to refer to the assembly of three different segments, or exclusively to long frontotemporal fibers. Similarly, the term SLF has been employed to denote the whole superior longitudinal associative system, or only the horizontal frontoparietal parts. As only partial correspondence can be identified among the available nomenclatures, and in the absence of an official designation of all anatomical structures that can be encountered in clinical practice, a high level of vigilance regarding the effectiveness of every oral or written act of communication is mandatory.

Fundamento: A análise prognóstica multivariada tem sido realizada tradicionalmente por modelos de regressão. No entanto, muitos algoritmos surgiram, capazes de traduzir uma infinidade de padrões em probabilidades. A acurácia dos modelos de inteligência artificial em comparação à de modelos estatísticos tradicionais não foi estabelecida na área médica. Objetivo: Testar a inteligência artificial como um algoritmo preciso na predição de doença coronariana no cenário de dor torácica aguda, e avaliar se seu desempenho é superior a do modelo estatístico tradicional. Métodos: Foi analisada uma amostra consecutiva de 962 pacientes admitidos com dor torácica. Dois modelos probabilísticos de doença coronariana foram construídos com os primeiros 2/3 dos pacientes: um algoritmo machine learning e um modelo logístico tradicional. O desempenho dessas duas estratégias preditivas foi avaliado no último terço de pacientes. O modelo final de regressão logística foi construído somente com variáveis significativas a um nível de significância de 5%. Resultados: A amostra de treinamento tinha idade média de 59 ± 15 anos, 58% do sexo masculino, e uma prevalência de doença coronariana de 52%. O modelo logístico foi composto de nove preditores independentes. O algoritmo machine learning foi composto por todos os candidatos a preditores. Na amostra teste, a área sob a curva ROC para predição de doença coronariana foi de 0,81 (IC95% = 0,77 -0,86) para o algoritmo machine learning, similar à obtida no modelo logístico (0,82; IC95% = 0,77 -0,87), p = 0,68. Conclusão: O presente estudo sugere que um modelo machine learning acurado não garante superioridade à um modelo estatístico tradicional

Background: Despite existing clinical, laboratory and electrocardiographic characteristics suggestive of acute pericarditis, there is no multivariate model as a diagnostic tool. Objective: To develop a clinical score for diagnosis of pericarditis as the cause of acute chest pain, using admission data. Methods: In a diagnostic case control study, we compared 45 patients of the Chest Pain Registry diagnosed of pericarditis (confirmed by magnetic resonance imaging or the presence of pleural effusion in echocardiography) versus 90 patients with an alternative confirmed diagnosis, randomly selected from our registry. Six clinical characteristics, 16 chest pain characteristics and 4 complementary exams were tested. Logistic regression was used to derivate a probabilistic model composed by independent predictors of pericarditis. Results: Among 17 variables associated with pericarditis, 5 remained independent predictors: age, pain aggravation with thorax movement; positive troponin; diffuse ST segment elevation and C reactive protein. Each independent predictor was attributed a score proportional to its regression coefficient. The final score presented discriminatory capacity represented by Cstatistic of 0,97 (95% CI = 0,93 to 1,0). The best cutoff point was defined as > 6 points, with sensitivity of 96% (95% CI = 85 to 100), specificity of 87% (95% CI = 78 to 93), positive likelihood ratio of 7,2 (95% CI = 4,2 to 12) and negative likelihood ratio of 0,05 (95% CI = 0,01 to 0,2). Conclusion: The proposed multivariate score is accurate for diagnosis of pericarditis. It needs to be further validated in an independent sample.

IntroductionCoronary anatomy is one of the strongest risk predictors in Acute Coronary Syndromes (ACS), which justifies early coronary angiography. Diagnostic scores for predicting outcomes are usually superior to clinical judgment. Despite being validated for prognosis, the GRACE score has been used to discriminate patients with high or low probability of anatomical severity.ObjectiveTo test the hypothesis that the GRACE score actually predicts anatomical severity.MethodsThe study was carried out by assessing consecutive patients with ACS who underwent invasive angiography. Severe anatomical disease was defined as obstructive involvement (≥ 70% in diameter) in (1) left main coronary artery or (2) double or triple vessel disease involving proximal left anterior descending artery or (3) subocclusion. The GRACE score was evaluated under numerical and dichotomous tests.ResultsA total of 733 patients were evaluated, aged 63 ± 14 years, 61% male and GRACE score of 119 ± 37. Obstructive coronary disease was observed in 81% of the patients, classified as one, two or three vessel disease, or left main coronary artery involvement in 28%, 23%, 26% and 4%, respectively. The area under the ROC curve of the GRACE score was 0.65 (95% CI = 0.61 - 0.69) for predicting severe disease. The cutoff point below which the first GRACE tertile is defined (109) was used to dichotomize low-risk (N = 318) and medium-high-risk (N = 415) samples. This standard definition of intermediate-high risk by the GRACE score (> 109) revealed sensitivity of 67% in detecting severe anatomy (95% CI = 61% - 72%) and specificity of 50% (95% CI = 46% - 55%), resulting in positive likelihood ratio of 1.3 (95% CI = 1.2 - 1.5) and negative likelihood ratio of 0.66 (95% CI = 0.55 - 0.80). There was a weak correlation between GRACE and anatomical scores such as SYNTAX (r = 0.36, P < 0.001) and Gensini (r = 0.36, P < 0.001).ConclusionDespite statistical association with extent of anatomical coronary disease, the GRACE Score is not accurate to predict severity of disease before coronary angiography.

IntroductionSpin is defined as an inaccurate interpretation of results, intentionally or not, leading to equivocal conclusions and misdirecting readers to look at the data in an overly optimistic way. Previous studies have shown a high prevalence of spin in scientific papers and this systematic review aims to investigate the nature and prevalence of spin in the neurosurgical trauma literature. Any associated factors will be identified to guide future research practice recommendations.Methods and analysisThe Preferred Reporting Item for Systematic Reviews and Meta-Analyses recommendations will be followed. Randomised clinical trials (RCTs) that enrolled only patients with traumatic brain injury and investigated any type of intervention (surgical or non-surgical) will be eligible for inclusion. The MEDLINE/PubMed database will be searched for articles in English published in 15 top-ranked journals. Spin will be defined as (1) a focus on statistically significant results not based on the primary outcome; (2) interpreting statistically non-significant results for a superiority analysis of the primary outcome; (3) claiming or emphasising the beneficial effect of the treatment despite statistically non-significant results; (4) conclusion focused in the per-protocol or as-treated analysis instead of the intention-to-treat results; (5) incorrect statistical analysis; (6) republication of a significant secondary analysis without proper acknowledgement of the primary outcome analysis result. Traditional descriptive statistics will be used to present RCT characteristics. Standardised differences between the groups with or without spin will be calculated. The variables with a standardised difference equal or above 0.2 and 0.5 will be considered weakly and strongly associated with spin, respectively.Ethics and disseminationThis study will not involve primary data collection and patients will not be involved.Trial registration number10.17605/OSF.IO/H3FGY.