dataesr/scientific-paragraphs-categorization huggingface dataset:
All these meetings were transcribed and form a 353page body of primary data. Additionally, to triangulate the data, we collected 760 pages of secondary data (see Table  2 ). These allowed us to gather accounts from victims and/or their families. To process our empirical data in a comprehensive way, we have used the dictionary of topics presented in Table  3. The first-order codes emerged from the field, while the second-order codes allowed us to progressively transition to theoretical concepts by grouping the various items identified. Once we established this data structure, we coded our entire empirical material with the Nvivo qualitative data analysis software. Second-order codes, derived from our iterative abstraction approach, reveal four key moments in paradox management (error reporting, announcement to the families, feedback and legal action) as well as two cross-cutting contextual factors (internal and external tolerance of error).
• Oversight bodies One Advising Doctor -Quality and Safety of Care and Support Cluster (ARS); one Technical Advisor -Improvement of Quality and Safety of Care Department (HAS) pages) categorized according to the grid presented in Table  3. The empirical results were then structured and drafted from this document. The words appearing in italics in the text are all quotes from our respondents. Similarly, our model (see Figure  1 ) -built at the same time as the data structure, that is, iteratively -reuses the six second-order codes. 4 4. We searched for as many articles as possible that included the terms 'erreur médicale' or 'événement indésirable (grave)', cross-referencing them with terms such as 'CHU-X', 'hôpital', 'faute pénale', 'punition', 'déclarer', etc. On this basis, we selected five articles that reported the three SAEs that occurred at CHU-X as well as three articles that were directly related to the subject of our research.
And the latter are generally 'satisfied' with having been informed, according to CHU-X professionals. While we were not able to interview the victims of the medical errors identified in this study, our secondary data lend credence to our respondents' claim. For example, here is what the father of a child who was the victim of several medical errors said in the documentary 'Que reste-t-il de nos erreurs?': 'Saying that a 9. In certain cases, the professionals directly concerned by the medical error are, despite it all, present during discussions with the family, but they will be accompanied by a mediator. mistake was made is rather necessary -in my opinion -to maintain trust, paradoxically. I think that honesty is an important element'. Such statements lend credence to the idea, defended by CHU-X, that families 'appreciate it' when medical staff show 'candor', and they are then more 'understanding' of mistakes.
Study overview ALTITUDES is a prospective nationwide clinicalebiological cohort of patients with incident DF diagnosed between January 2016 and February 2021 and confirmed by central pathological review. 1  The study inclusion criteria were as follows: (i) incident cases of DF diagnosed in France, (ii) diagnosis confirmed by a pathology review conducted by the French Sarcoma Group, (iii) affiliated to National Health Insurance, and (iv) the provision of signed informed consent by the patient or his or her legal guardians (both parents' signatures were required in adolescent patients). The study exclusion criteria were as follows: (i) deprivation of liberty and (ii) patients who were unable or unwilling to provide consent. The ALTITUDES study does not include specific therapeutic interventions, but instead provides prospective, observational data collection and biobanking (i.
e. tumor samples, blood samples, and baseline imaging scans). The ALTITUDES study is purely descriptive and hypothesis-generating, and there was no formal sample size calculation. For the present study, we selected female patients aged 16-45 years at the time of DF diagnosis who were diagnosed with unifocal DF. The enrolled patients were treated with initial surgery or active surveillance as the initial treatment approach. For the present analysis, we excluded patients who had received hormonal therapy for reasons other than contraception (e. g. for adjuvant treatment of breast cancer). All data were collected prospectively, with the exception of the precise nature of hormonal contraception, as that required retrospective data collection during the brief planned visits or phone interviews.
This study was approved by the ethics committee at CPP Nord-Ouest I (the French governmental organization for the protection of persons in biomedical research; 3 December 2015) and by the French Drug Agency (ANSM; 20 November 2015). This study was conducted in accordance with the principles of the Declaration of Helsinki and written informed consent was obtained from each patient. This investigation was registered under the following clinical trial registration number: NCT02867033.
Endpoints As previously published by us,  11, 12  the primary endpoint evaluated in this study was event-free survival (EFS). The events considered in this study were local relapse after complete (R0/R1) resection, disease progression (according to the local investigator/evaluating physician) during active surveillance, or death. Data and EFS were reported in both therapeutic groups (i. e. the active surveillance and surgery groups) and for the overall study population.
To illustrate these results, we generated EFS curves estimated using the multivariate models. For each type of hormonal exposure, we evaluated the association between recent history of hormonal exposure (the period of exposure and the 24-month post-exposure period versus no exposure) and EFS according to the front-line treatment strategy, tumor site, and tumor size by including an interaction term in the multivariate models. We illustrated these subgroup analyses using forest plots. All estimates are reported with their 95% confidence intervals (95% CIs). Analyses were carried out with a twosided a level of 5%. Statistical analyses were conducted using STATA/SE statistical software (v. 15. 1; StataCorp, LP, College Station, TX).
ACKNOWLEDGEMENTS The authors would like to thank the patients and families for their participation in this study; the staff members involved in study management, in particular  Emilie Decoupigny  and  Marie Vanseymortier  from the sponsorship unit at  Centre Oscar Lambret (Lille University ); all investigators and their teams who participated in the study; the patient advocacy group 'SOS Desmoïde';  Guillemette Antoni , a biostatistician from  CESP  ( INSERM Villejuif ), for her advice;  Françoise Bonichon  for her help in the literature research; and the data-managers from  Centre de Traitement des Données du Cancéropôle Nord-Ouest (CTD-CNO ), who oversaw the study data management; the  CTD-CNO  clinical research platform was funded by the  French National Cancer Institute (INCa)  and  'La Ligue Nationale Contre le Cancer '.
The implementation of this system relies on the approach proposed in  (Da San Martino et al. , 2019). The most recent approaches for propaganda detection are based on language models that mostly involve transformer-based architectures. The approach that performed best on the NLP4IF'19 sentence-level classification task relies on the BERT architecture with hyperparameters tuning without activation function  (Mapes et al. , 2019). (Yoosuf and Yang, 2019)  focused first on the pre-processing steps to provide more information regarding the language model along with existing propaganda techniques, then they employ the BERT architecture casting the task as a sequence labeling problem. The systems that took part in the SemEval 2020 Challenge -Task 11 represent the most recent approaches to identify propaganda techniques based on given propagandist spans.
The most interesting and successful approach  (Jurkiewicz et al. , 2020)  proposes first to extend the training data from a free text corpus as a silver dataset, and second, an ensemble model that exploits both the gold and silver datasets during the training steps to achieve the highest scores. As most of the above mentioned systems, also PROTECT relies on language model architectures for the detection and classification of propaganda messages, empowering them with a rich set of features we identified as pivotal in propagandist text from computational social science literature  (Vorakitphan et al. , 2021). In particular,  (Morris, 2012)  discusses how emotional markers and affect at word-or phrase-level are employed in propaganda text, whilst  (Ahmad et al. , 2019)  show that the most effective technique to extract senti-ment for the propaganda detection task is to rely on lexicon-based tailored dictionaries.
Datasets To evaluate the approach on which PROTECT relies, we use two standard benchmarks for Propaganda Detection and Classification, namely the NLP4IF'19  (Da San Martino et al. , 2019)  and SemEval'20 datasets  (Da San Martino et al. , 2020a  Those classes are not uniformly distributed in the data sets. Loaded-Language and Name-Calling Labeling are the classes with the 2 https://propaganda. qcri. org/nlp4if-s hared-task/ 3 https://propaganda. qcri. org/semeval2 020-task11/ higher number of instances (representing respectively 32% and 15% of the propagandist messages on all above-mentioned datasets). The classes with the lower number of instances are Whataboutism, Red-Herring, Bandwagon, Straw-Men, respectively occurring in 1%, 0. 87%, 0. 29%, 0. 23% in NLP4IF'19 datasets. In SemEval'20T11 such labels where merged, and the classes Whataboutism Straw-Men Red-Herring, Bandwagon respectively represent 1.
04 F1 with respect to sigmoid). 7 https://huggingface. co/transformers/ model doc/roberta. html form both a sentence classification and a span classification. More precisely: i) we input a sentence to the tokenizer where max length is set to 128 with padding; ii) we input the span provided by the propaganda span-template from SemEval T11 dataset, and we set max length value of 20 with padding. RoBERTa tokenizer is applied in both cases. If a sentence does not contain propaganda spans, it is labeled as "none-propaganda". To take into account context features at sentence-level, a BiLSTM is introduced. For each sentence, semantic and argumentation features are extracted following the methodology proposed in  (Vorakitphan et al. , 2021)  and given in input to the BiLSTM model (hyper-parameters: 256 hidden size, 1 hidden layer, drop out of 0.
1 with ReLU function at the last layer before the joint loss function). Such features proved to be useful to improve the performances of our approach on propagandist messages classification, obtaining SoTA results on some categories (in  (Vorakitphan et al. , 2021)  we provide a comparison of our model with SoTA systems on both NLP4IF and SemEval datasets). To combine the results from sentence-span based RoBERTa with the feature-based BiLSTM we apply the joint loss strategy proposed in  (Vorakitphan et al. , 2021). Each model produces a loss per batch using CrossEntropy loss function L. Following the function: loss joint loss = α × (loss sentence +lossspan+loss semantic argumentation features ) N loss where each loss value is produced from CrossEntropy function of its classifier (e. g. , loss sentence and loss span from RoBERTa models of sentence and span, loss semantic argumentation features from the BiLSTM model.
) To train the above mentioned methods for the propaganda technique classification task, we merged the data sets of NLP4IF'19 and Se-mEval'20 T11 (same setting as in Step 1). Then we tested the full pipeline of PROTECT on the development set from Semeval'20 T11. The output of the snippet detection task (Step 1) are provided as a span-pattern to the models performing Step 2. Table  1  reports on the obtained results of the full pipeline (Step 1+Step 2) averaged over 5 runs (we cannot provide a fair comparison of those results with SoTA systems, given that in SemEval the two tasks are separately evaluated and no pipeline results are provided). We can notice however, that our results in a pipeline are comparable with the Given the high complexity of the propaganda technique classification task and the classes' unbalance, some examples are miss-classified by the system.
Acknowledgments This work is partially supported by the  AN-SWER  project  PIA FSN2  n. P159564-2661789/DOS0060094  between Inria and Qwant. This work has also been supported by the  French government , through the  3IA Côte d'Azur Investments in the Future  project managed by the  National Research Agency (ANR)  with the reference number  ANR-19-P3IA-0002.
Selection of Cross-Reactive Monoclonal Antibody Candidates and Cloning of Fusion Proteins Protein sequence alignments and determination of homology between murine and porcine Blimp-1, IRF4, and Pax5 sequences were performed by the NCBI BLAST tool  (43)  and BioEdit sequence alignment editor software (version 7. 0. 5. 3, available at https://bioedit. software. informer. com/7. 0/). For testing of putative cross-reactive antibody candidates, recombinant fusion proteins of porcine Blimp-1, IRF4, or Pax5 with a C-terminal FLAG tag were generated and expressed in HEK293T cells. RNA extraction and cDNA synthesis of lymphocytes isolated from lung tissue (Blimp-1) or PBMC (IRF4, Pax5) were done as described by Lagler et al. (44). For PCRs, genespecific primers with restriction overhangs were designed (Table  1 ).
6. 2. 8. 1, Thermo Fisher Scientific). Further, cells were labeled with mAbs directed against CD49d and CD172a, as indicated in Table  2. Labeled cells were sorted into two populations: CD3 - CD16 -CD172a -CD49 +/-and CD3 -CD16 -CD172a -CD49 high FSC-A high. Sorting purity was at least 88% or higher for the CD49d high -sorted population, and above 97% for CD49d -/+ cells. Sorted cells were either forwarded to IgG ELISpot or used in downstream FCM analysis (see below). Sorting and FCM analysis were performed using a BD FACS Aria ™ (BD Biosciences, equipped with three lasers) with FACSDiva software version 6. 1. 3 (BD Biosciences).
After fixation and permeabilization, cells were stained for intracellular markers, followed by secondary isotype-specific antibodies or streptavidin conjugates where applicable. For identification of Ig classes in PCs, staining was likewise carried out intracellularly, as recommended by the guidelines for FCM and cell sorting  (26). Antibodies and conjugates used are listed in detail in Table  2. Fluorescence minus one (FMO) stainings with isotype-specific control antibodies for the PC panels from a representative animal are shown in Supplementary Figure  1. Samples of the ex vivo phenotyping were measured using a CytoFLEX LX flow cytometer (Beckman Coulter GmbH, Krefeld, Germany, equipped with six lasers) 1 day after staining. Data were acquired with CytExpert software (version 2. 3, Beckman Coulter).
ACKNOWLEDGMENTS The authors greatly acknowledge the staff of the  University Clinic for Swine at the University of Veterinary Medicine Vienna, Austria , for animal handling and sample selection. We thank  Dr. Barbara C. Rütgen ,  Clinical Pathology Platform at the University of Veterinary Medicine Vienna, Austria , for taking the Diff-Quik pictures. The authors thank  Egbert Mundt ,  Boehringer Ingelheim Animal Health , for thorough reading of the manuscript.
Currently, there are multiple research projects to improve the overall quality of care both inside and outside of healthcare centers, establishing dedicated care pathways for multimorbid patients  [4] [5] [6]. Barnett et al. [7]  reported an association between age, sex, deprivation and multimorbidity based on a list of 40 medical conditions. This list was built using policy recommendations and important chronic conditions identified in  [8]. However, counting conditions can be quite limiting, and are a controversial measure of multimorbidity for these studies  [9]. [10]  highlighted the importance of using a standard measure of multimorbidity to analyze and compare the results of studies in which different scores have been built to describe multimorbidity. The most common measure of multimorbidity is the Charlson comorbidity index score, originally introduced in 1987  [11]  and first updated in 1994  [12]  and numerous times since to be applied with administrative databases  [13, 14]  or to predict other outcomes  [15].
The hospital frailty risk score  [16]  was developed to identify older patients presenting frailty diagnoses. A higher risk score is associated with a higher risk of adverse outcomes and a higher use of medical resources. Weights were calculated using logistic regression targetting of the identified frail population and validated for the prediction of adverse outcomes. Calderon-Larrañaga  [17]  explored a different approach. To build this multimorbidity score,  [17]  gathered a panel of medical experts to group diagnoses into categories according to "clinical criteria and relevance (pathophysiological pathway, treatment, prognosis, and prevalence)" and defined the score of a patient as the number of categories where the patients had at least one diagnosis. In order to capture the multimorbidity by using all diagnosis information, we built a database containing the exhaustive list of diagnoses made to the patient during their stay (3-digit ICD-10 codes) to compare the performance obtained by creating thematic groups of diagnoses.
We will refer to these nonaggregated versions of the scores as Calderon-Larrañaga portfolio and HFRS portfolio in the remainder of the paper. In the portfolio versions of the measures the category information was coded as a binary value equal to 1 if the category/diagnosis was active for the patient. We chose HFRS because it was designed to predict frailty and was validated for the prediction of 30-day readmission  [16]. As mentioned before we built a database containing an exhaustive list of diagnoses made during the patients' stay (3-digit ICD-10 codes). It is important to note that for the readmission analysis we excluded patients who died during their initial hospitalization, as readmission is not applicable for deceased patients. This resulted in the exclusion of 780 patients from the readmission database.
However, we kept the data from these patients to predict length of stay. Palliative care or complex pathologies and care resulting in the death of the patient might be associated with longer hospital stays. However, it is also important to note that for this study we only have access to the patients' data during their hospitalization, which means that we do not have access to their status after one year. The machine learning methods used in this study for prediction of hospital readmission were tree classifier, a random forest classifier and a k-nearest neighbor classifier. For the length of stay prediction we used a tree regressor and a random forest regressor. All experiments were performed using Python 3. 7, pandas  [25, 26]  and scikit-learn  [27]. For all learning experiments, the data were split between training and testing samples, with the training sample representing 75% of the original dataset.
We used a grid search with crossvalidation to parametrize the learning algorithms. The parameters that were tested and optimized were the depth and number of leaves for the decision tree approach, the depth, number of leaves and number of estimators for the random forest approach and the algorithm, leaf size and number of neighbors for the k-nearest neighbors approach. As previously mentioned, we used the patients' anonymous identifiers to identify the patients stays. In addition to diagnosis information, we used the age, sex, length of stay, ED admission information and number of steps in the pathway to predict the same-hospital readmission within 30 days and 365 after discharge of the initial stay from the inpatient database. Those variables and the residential zip code (except the length of stay), were used for the prediction of length of stay.
The class size of readmitted patients within 30 days (1 965, or 15. 86% of the stays) appeared to be far smaller than that of nonreadmitted patients. To address these imbalanced classes, we performed resampling techniques on the dataset using the imbalanced-learn Python package presented in  [28]  and used appropriate metrics to evaluate the results. Resampling is a method that changes the composition of the dataset to allow training on a balanced dataset. There are techniques that delete samples from the majority class, others that generate samples of the minority class and some that combines the two. We used the imbalanced-learn Python package presented in  [28]. The different methods were applied to train the learning algorithm on the balanced dataset, and we selected the best performing combination of resampling and learning algorithm.
For the prediction of hospital readmission within 30 days we used the same 3 classifier algorithms: decision tree, random forest and k-nearest neighbor. Some metrics such as accuracy are not an appropriate target to assess the performance of algorithms when dealing with unbalanced datasets. Therefore, we decided to focus on the F1-score, a weighted average of precision and recall, and receiving operating characteristic area under the curve (ROC-AUC) which compares sensitivity and specificity. Thus, we will have a better understanding of the classifier's efficiency. To evaluate regression algorithms, we used two well-known metrics: mean absolute error and mean squared error. The classifiers' performances were assessed using accuracy (percentage of correctly predicted instance) and F1-score for rehospitalization within 365 days and receiver operating characteristic area under curve and F1-score for hospitalization within 30 days, where patients were unevenly distributed between positive and negative classes.
Database Table  1  presents the general variables used by the prediction algorithm. Additionally the algorithms use information on the diagnoses made to the patients. Five versions of this database were coded in order to compare the diagnoses information and aggregated multimorbidity scores: 1. using raw information on the diagnoses: one column per diagnoses with binary values; 2. using Calderon-Larrañaga score; 3. using Calderon-Larrañaga portfolio (as explained in section Statistical Analysis Methodology); 4. using the HFRS score; and 5. using the HFRS portfolio. These features were used to train and test prediction models targeting (i) readmission within 365 days, (ii) readmission within 30 days after the end of the initial hospitalization and (iii) the total length of stay.
Epidemiological description As specified above, we include patients above 60 years old with diagnoses in 2 different categories of ICD-10 classification who were discharged in 2017 in our study. This database includes 12 391 hospital stays of 8 882 unique patients (4 541 male patients and 4 341 female patients). The distribution of ages for this population is shown in Fig 1. On the left side of the graph are male patients, and on the right side of the graph are female patients. We observe that the mean age for both male and female patients is quite high (74. 73 and 77. 93 respectively), with a median (74 and 79 years old, respectively). In our study, it was more likely that older multimorbid people were more susceptible to hospitalization. We calculated the age-adjusted Charlson comorbidity index score  [12] , the Calderon-Larrañaga score  [17]  and the HFRS  [16]  for each patient.
The mean age-adjusted Charlson's score was 5. 65, the mean Calderon-Larrañaga's score was 4. 94 and the mean HFRS was 6. 32. The mean absolute difference between age-adjusted Charlson comorbidity and Calderon-Larrañaga scores was 2. 39, which shows how the different scores highlight different multimorbidity profiles. Table  2  shows the ten ICD-10 codes that are the most frequently diagnosed in the 12 391 stays of the database. Eight out of these 10 codes refer to chronic diseases, among them we can notice hypertension, that appears in 51. 6% of the stays, type 2 diabetes mellitus (25. 7%), overweight and obesity (13. 4%), and chronic ischemic heart diseases (13. 3%).