Corrado Tommasi Crudeli †

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TikTok content as a source of health education regarding epicondylitis: a content analysis

Purpose This study aimed to assess the validity and informational value of TikTok content about epicondylitis. The hypothesis tested herein was that TikTok video content would not provide adequate and valid information. Methods The term "epicondylitis" was used as a keyword to comprehensively search for TikTok videos, and the first 100 videos that were retrieved were subsequently included for analysis. The duration, number of likes, number of shares and number of views were recorded for each video. Furthermore, the videos were categorized on the basis of their source (medical doctor, physiotherapist, or private user), type of information (physical therapy, anatomy, clinical examination, etiopathogenesis, patient experience, treatment, or other), video content (rehabilitation, education, or patient experience/testimony), and the presence of music or voice. Assessments of video content quality and reliability were conducted using the DISCERN tool, the Journal of the American Medical Association (JAMA) benchmark criteria, and the Global Quality Score (GQS). Results A total of 100 videos were included in the analysis: 78 (78.0%) were published by physiotherapists, 18 were published by medical doctors (18.0%), and 4 were published by private users (4.0%). Most of the information pertained to physical therapy (75; 75.0%) and most of the content was about rehabilitation (75; 75.0%). The mean length of the videos was 42.51 +/- 24.75 seconds; the mean number of views was 193,207.78 +/- 1,300,853.86; and the mean number of comments, likes, and shares were 22.43 +/- 62.54, 1578.52 +/- 8333.11, and 149.87 +/- 577.73, respectively. The mean DISCERN score, JAMA score, and GQS were 18.12 +/- 5.73, 0.80 +/- 0.53, and 1.30 +/- 0.52, respectively. Videos posted by medical doctors/private users had higher scores (p < 0.05) than videos posted by physiotherapists. Videos that focused on education or patient experience had higher scores (p < 0.05) than videos based on rehabilitation. Conclusions TikTok can be an unreliable source of information regarding epicondylitis treatment. It is common to find nonphysicians who share medical advice on the platform, with medical treatments demonstrating the weakest level of supporting evidence. Elbow surgeons should advise their patients that treatment recommendations from TikTok may not align with established guidelines.Level of Evidence: Level IV-Cross-sectional study

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018

On behalf of the Program Committee, a very warm welcome to the Fifth Italian Conference on Computational Linguistics (CLiC-­‐it 2018). This edition of the conference is held in Torino. The conference is locally organised by the University of Torino and hosted into its prestigious main lecture hall “Cavallerizza Reale”. The CLiC-­‐it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after five years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges

Die Malaria nach den neuesten Forschungen

Layoutgetreues Digitalisat der Ausg.: Berlin ; Wien : Urban & Schwarzenberg, 1900 Standort: Fachgebiet für Geschichte der Medizin (192) Signatur: 226 Provenienz: Behring, Emil vo

The New Prophylaxis Against Malaria: An Account of Experiments in Latium.

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