Bridging the gap: unveiling key links between autism and anxiety symptoms in autistic children and youth using a network analysis in pooled data from four countries.

Background: Autistic children experience significantly higher rates of anxiety compared to nonautistic children. The precise relations between autism characteristics and anxiety symptoms remain unclear in this population. Previous work has explored associations at the domain level, which involve examining broad categories or clusters of symptoms, rather than the relationships between specific symptoms and/or individual characteristics.

Using machine learning to identify speech markers of subjective cognitive impairment in breast cancer survivors.

Purpose: Cancer-related cognitive impairment (CRCI) refers to cognitive changes described by cancer patients. While subjective cognitive difficulties in survivors are well screened by questionnaires, CRCI remains seldom diagnosed by neuropsychological tests. New objective approaches are needed to detect CRCI.

Capillary CRP in primary care in France: an observational study.

Background: Antibiotic resistance causes 700,000 deaths annually worldwide. In France, diagnostic uncertainty in consultations leads to inappropriate prescribing of antibiotics, which increases antibiotic resistance. Capillary C-reactive protein (CRP) machines can provide a CRP measurement in primary care in two minutes and can help general practitioners in their prescribing decisions.

Pleural Effusions Requiring Thoracocentesis Are Associated With Baseline Lung Allograft Dysfunction and Mortality in Lung Transplant Recipients.

Background: Pleural effusions of unknown etiology have been demonstrated to be associated with poor prognosis in lung allograft recipients. We aimed to identify novel risk factors for pleural effusions after lung transplantation (LTX) and to shed light on their association with allograft function and survival, differentiating early and late pleural effusions after LTX.

Methods: We performed a retrospective study of all LTX recipients transplanted at the LMU Klinikum Munich from 2013 to 2018.

Automatic analysis of negation cues and scopes for medical texts in French using language models.

Objective: Correct automatic analysis of a medical report requires the identification of negations and their scopes. Since most of available training data comes from medical texts in English, it usually takes additional work to apply to non-English languages. Here, we introduce a supervised learning method for automatically identifying and determining the scopes and negation cues in French medical reports using language models based on BERT.