Publications by authors named "A S Jannot"

This paper introduces a prognostic method called FLASH that addresses the problem of joint modeling of longitudinal data and censored durations when a large number of both longitudinal and time-independent features are available. In the literature, standard joint models are either of the shared random effect or joint latent class type. Combining ideas from both worlds and using appropriate regularization techniques, we define a new model with the ability to automatically identify significant prognostic longitudinal features in a high-dimensional context, which is of increasing importance in many areas such as personalized medicine or churn prediction.

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Background: Suboptimal treatment delays is known to impact prognosis of patients with cancer but optimal timing in specific subgroups remains poorly studied. This study aimed to analyze treatment delays in young women treated for a breast cancer (BC) on and its impact on their prognosis using French Nationwide Data.

Methods: Using the CAREPAT-YBC Cohort based on the French National Healthcare System Database, we analyzed disease-free survival (DFS) in 22,093 young women (18-45 years) who underwent either surgery-chemotherapy-radiotherapy pathway (adjuvant setting, 15,433 patients) or chemotherapy-surgery-radiotherapy pathway (neoadjuvant setting, 6660 patients), according to delays between the different pathways.

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Background: Limited data are available on long-term respiratory disabilities in patients following acute COVID-19.

Patients And Methods: This prospective, monocentric, observational cohort study included patients admitted to our hospital with acute COVID-19 between 12 March and 24 April 2020. Clinical, functional and radiological data were collected up to 28 months after hospital discharge.

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Background/aim: We aimed to assess gastrointestinal cancers risks in a large cohort of individuals with primary antibody deficiency (PAD) and their association with risk of autoimmune and inflammatory gastrointestinal diseases.

Methods: Investigating a French national database of inpatient admissions between 2010 and 2018, we identified 12,748 patients with PAD and 38,244 control non-exposed individuals. We performed multiple exposed-non-exposed studies using conditional logistic regression.

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