dataesr/scientific-paragraphs-categorization huggingface dataset:
Con dentiality {27} SPIRIT guidance: How personal information about potential and enrolled participants will be collected, shared, and maintained in order to protect con dentiality before, during, and after the trial. All study-related information will be stored securely at the sites. All participant paper information will be stored in locked le cabinets in areas with limited access provided by the local PIs. All laboratory specimens different from those included in the SoC, reports, process, and administrative forms will be identi ed only by an ID to maintain participant con dentiality. All records that contain names or other personal identi ers, such as locator forms and ICFs, will be stored separately from study records identi ed by the ID number. All local databases will be secured with encrypted and password-protected access systems.
Forms, lists, logbooks, appointment books, and any other listings that link participant ID numbers to other identifying information will be stored in a separate, locked le in an area with limited access managed by the local PIs. All HIV test results will be kept strictly con dential; all counseling will be conducted following local privacy customs. Participants' study information will not be released outside of the study consortium without the written permission of the participant. CTU will oversee the intra-study data sharing process, and they will be given access to the cleaned data sets. Project data sets will be housed in the CTU secure server allocated in Hospital 12 Octubre (Madrid, Spain), and all data sets will be encrypted and password protected. Project PIs will have direct access to their own site's data sets and will have access to other sites' data by justi ed request.
To ensure con dentiality, data dispersed to study staff will be blinded to any identifying participant information. RedCap software implements an audit trail to ensure the data access tracking by the study personnel. All records identifying the participant will be kept con dential and, to the extent permitted by the applicable laws and/or regulations, will not be made publicly available. Procedures for data collection, storage, transfer, protection, and retention will be detailed in the trial Data Management Plan. Speci cally, designed CRFs will be developed for collection of the trial's data and will be designed in compliance with regulatory requirements for clinical trials, following the principles of ICH-GCP and the General Data Protection Regulation 2016/679. A centralized computerized database, managed by the CTU and allocated in the Hospital 12 Octubre (Madrid, Spain), will be the central data repository for all sites participating in the trial.
The data controller is SERMAS-FI+12. The processing of personal data is on the basis of the informed consent. The participant will have the right to access, rectify or erase the child´s personal data; restrict the types of activities the research team can do with; object to using your and your child´s personal data for speci c types of activities; or withdraw your consent. No directly identi able patient data will be held in the trial database, and the patient will be identi ed by a trial ID number. However, data that could be considered as indirectly identi able (date of birth) or sensitive (date of medical events) will be handled within the guidelines of CTU standards procedures, which also details the sending and receiving of patient data under secure channels, either encrypted zip les or using inbuilt encryption software for email.
Each local site will be responsible for ensuring that the samples are appropriately labeled in accordance with the trial procedures and comply with the 1998 Data Protection Act. Biological samples collected from participants as part of this trial will be transported, stored, accessed and processed in accordance with national guidelines/legislation relating to the use and storage of human tissue for research purposes and such activities shall at least meet the requirements as set out in the 2004 Human Tissue Act and the 2006 Human Tissue (Scotland) Act. Full blood count, chemistry, HIV viral load, and CD4 samples Samples will be processed locally using the ongoing laboratory methods in each site. Those samples that coincide in the calendar with the routine controls of the children will not be repeated but the results will be registered in the database and a copy of the source document will be kept.
Sensitivity analysis will be performed to ensure low imputation derived bias and an imputation appendix will be assessed in the study report. Finally, sensitivity analysis will then be undertaken to assess the robustness of the conclusions to assumptions regarding the missing data. Plans to give access to the full protocol, participant level-data and statistical code {31c} SPIRIT guidance: Plans, if any, for granting public access to the full protocol, participant-level dataset, and statistical code. No later than three years after the collection of the 1-year post-randomization visits the CTU will deliver a pseudo-anonymized data set and metadata to an appropriate data archive for sharing purposes unless speci c national legislation from any of the sites impedes sharing open access of the data.
Randomization The Trial Management Group will be supported by the Clinical Trial Unit of the Fundación para la Investigación Biomédica Hospital Universitario 12 de Octubre (F+12-CTU). This CTU will act as the Clinical Trial Unit of the clinical trial. Composition of the data monitoring committee, its role and reporting structure {21a} SPIRIT Composition of data monitoring committee (DMC); summary of its role and reporting structure; statement of whether it is independent from the sponsor and competing interests; and reference to where further details about its charter can be found, if not in the protocol. Alternatively, an explanation of why a DMC is not needed. Standard processes will be implemented to improve the accuracy of data entry and coding, including: Site quali cation and a pre-study visit will be performed by the CTU.
Central monitoring The FI+12-CTU will perform a secondary study database clean-up working closely with the participating sites to ensure that all collected data has been received and that all quality control checks were performed. The DMT will develop a standard data de nition table (DDT) or Master le which will be used to consolidate all sites' data into a consistent format for storage in the central database. The DMT will maintain a specimen tracking process and link them to clinical data. The DMT will work closely with data managers from the sites to assure proper quality control checks are performed at the site level based on the central data editing plan. Data integrity will be validated by producing standard reports and distributing them to the sites on a routine basis. Standard processes will be implemented by local study personnel to enhance data quality and reduce bias by detecting and reducing the amount of missing or incomplete data, inaccuracies, and excessive variability in measurements.
Data entry and error checking will be completed in a timely manner to maximize the ability to resolve detected errors. DMT will perform an exhaustive quality control report checking for missing, unusual values, and clinical inconsistencies. Further outlier analysis will be performed in order to check possible data entry errors. If any such problems are identi ed, the site will be contacted and asked to verify or correct the entry. Changes will be made in the hard-copy CRF (if the site uses paper) and in the eCRF and entered into the database at the site. FI+12-CTU will also send reminders for any overdue and/or missing data with the regular inconsistency reports of errors. Each site will be responsible for developing a data entry error checking code, based on the standard data editing plan provided by the DMT.
The sites will be responsible for following up on any queries produced by this code, to ensure the data are as complete and accurate as possible. All queries must be resolved within two weeks unless a speci c date of resolution is requested by the DMT. Quality controls will be conducted for the rst ve patients of each site and afterward, at least every three months. A single query table will be sent to each site in order to identify possible data entry errors. Changes will be made in the hard-copy CRF (if the site uses paper), the eCRF and entered into the database at the site. FI+12-CTU will also send reminders for any overdue and/or missing data with the regular inconsistency reports of errors. The investigator must assure that patients' anonymity will be maintained and that their identities are protected from unauthorized parties.
Patients will be assigned a trial identi cation number, and this will be used on CRFs; patients will not be identi ed by their names. The investigator will keep securely a patient trial register showing identi cation numbers, names, and dates of birth. Data will be structured according to a nal repository. A nal repository will be chosen for anonymized data sharing, and transparency after the trial is closed, according to the funder (EDCTP) rules and recommendations, unless national laws impede it.
Direct access may include examining, analyzing, verifying, and reproducing any records and reports that are important to the evaluation of the study. Site visit logs will be maintained at the study site to document all visits. Monitoring of accurate data recording in sites Dedicated monitors from a CRO will visit all the clinical sites to validate and monitor data. They will conduct regular contact with sites and interim on-site monitoring visits. Site monitoring visits will be conducted to assess compliance with ICH-GCP guidelines. Study monitors will visit the site to: Verify compliance with human subjects and other research regulations and guidelines Assess adherence to the study protocol and study-speci c SOP Con rm the quality and accuracy of the information collected at the study site and entered into the study database Assess the resolution of any past or ongoing issues identi ed at previous monitoring visits The following data should be veri able from source documents: Screening log.
The causality assessment given by the local investigator at the hospital cannot be overruled; in the case of disagreement, both opinions will be provided in any subsequent reports. The CTU will review all reported AEs at least 6-monthly. Safety reports will be sent to the DSMB at least 6-monthly and will include a detailed analysis of study progress, data and safety issues. The CTU will also keep all investigators informed of any safety issues that arise during the trial. CTU will prepare annual Development Safety Update Reports (DSURs) which will be submitted annually to the Competent Authorities and Ethics Committees in each country participating in the trial. Any subsequent events that may be attributed to treatment should also be reported to national reporting schemes where relevant. The local PIs will record all SAEs.
Review process Each paper or abstract must be submitted to the Executive Committee for review of its appropriateness and scienti c merit before submission, giving enough time for review of a manuscript or journal article for review of any poster, presentation, abstract, or other written or oral material derived from the research. The Executive Committee may recommend changes to the authors and will nally submit its recommendations. Primary outcome papers EDCTP expects that grant holders will disclose the summary results of the study within 12 months from primary study completion (the last visit of the last subject for the collection of data on the primary outcome). The primary outcome papers will be those that present outcome data. The authorship of the publications will be decided by the Executive Committee and by EDCTP rules.
Publication in a journal is expected within 24 months from study completion, according to EDCTP rules. The Trial identi cation number or registry identi er code should be included in all publications of clinical trials and should be provided as part of the abstract to PubMed and other bibliographic search databases for easy linking of trial-related publications with clinical trial registry site records. This is essential for linking journal publications with registry records. Bene ciaries must acknowledge EDCTP funding as listed in the Grant Agreement. The bene ciaries must note express the contribution in each publication by acknowledgment or co-authorship in by the EDCTP rules. The nal report to EDCTP must include a report on the status of posting results in the study registry (including timelines when the nal posting of results is scheduled after the end of the funding period).
Acknowledgements Authors' contributions Funding Acknowledgements Trials guidance: Please acknowledge anyone who contributed towards the article who does not meet the criteria for authorship including anyone who provided professional writing services or materials. Authors should obtain permission to acknowledge from all those mentioned in the Acknowledgements section. See our editorial policies for a full explanation of acknowledgements and authorship criteria. If you do not have anyone to acknowledge, please write "Not applicable" in this section. Group authorship (for manuscripts involving a collaboration group): if you would like the names of the individual members of a collaboration Group to be searchable through their individual PubMed records, please ensure that the title of the collaboration Group is included on the title page and in the submission system and also include collaborating author names as the last paragraph of the "Acknowledgements" section.
Safety Monitoring This protocol has extensive safety monitoring in place. The study site investigators will be responsible for close safety monitoring of all children participating in the study, and for alerting the protocol team if unexpected concerns arise. Safety coordinator together with the central data manager will perform biweekly safety data validation and include the queries in the data validation form. All queries must be resolved within two weeks unless a speci c date of resolution is requested from the Safety Coordinator. Each participating child will be evaluated by a study clinician at each study visit. If a child misses a study visit, home visits will be conducted by trained study staff to ensure clinical evaluation. Every effort will be made to trace all children in the study for the nal outcome assessment.
As needed, children in the study may be evaluated at interim visits and/or referred for additional care. SAEs will also be regularly reviewed by the Safety Coordinator, and CTU compiled into reports for the DSMB every 6 months. The DSMB will be intimately involved in regular safety monitoring. Adverse event reporting and harms {22}. Site PIs are responsible for notifying and send reports to the Ethics Committees and Regulatory Authorities all SAEs. 9. Site PIs are responsible to assign the type of AE/AR, and initial expectedness. However, all efforts will be done to contact the CTU to agree on the early (<24 hours) report as much as possible. In each site, site PIs will ensure that notable AEs and ARs are recorded in line with the requirements of the protocol. Researchers will collect and verify all AEs, SAEs, SARs, and SUSARs according to this protocol onto a database.
Availability data and materials {29} SPIRIT guidance: Statement of who will have access to the nal trial dataset, and disclosure of contractual agreements that limit such access for investigators. Trials guidance: Please do not include any baseline or pilot data in your study protocol. The Editorial O ce will ask you to remove this if it is included. Please declare here who will have access to the nal trial dataset and disclose contractual agreements that limit such access for investigators. CTU will oversee the intra-study data sharing process, and they will be given access to the cleaned data sets. Project data sets will be housed in the CTU secure server allocated in Hospital 12 Octubre (Madrid, Spain), and all data sets will be encrypted, and password protected. Project PIs will have direct access to their own site's data sets and will have access to other sites' data by justi ed request.
To ensure con dentiality, data dispersed to study staff will be blinded to any identifying participant information. RedCap software implements an audit trail to ensure the data access tracking by the study personnel. The scienti c integrity of the project requires that the data from all the sites be analyzed study-wide and reported as such. Thus, an individual center is not expected to report the data collected from its center alone. All presentations and publications are expected to protect the integrity of the major objective(s) of the study. No later than three years after the collection of the 1-year post-randomization visits the CTU will deliver a pseudo-anonymized data set and metadata to an appropriate data archive for sharing purposes unless speci c national legislation from any of the sites impedes sharing open access of the data.
Title {1}"Empirical treatment against cytomegalovirus and tuberculosis in HIV-infected infants with severe pneumonia: study protocol for a multicenter, open-label, randomized controlled clinical trial" Trial ClinicalTrials. gov, NCT03915366, Universal Trial Number U111-1231-4736, Pan African Clinical Trial Registry registration PACTR201994797961340, Eudra CT 2019-001749-42. {2a and 2b}. Protocol 2. 0, January 2021 version {3} Funding {4} EDCTP (Grant number: RIA2017MC-2013 EMPIRICAL): 7. 680. 618,75 € Introduction Background and rationale {6a}.
Modern classification models tend to struggle when the amount of annotated data is scarce. To overcome this issue, several neural fewshot classification models have emerged, yielding significant progress over time, both in Computer Vision and Natural Language Processing. In the latter, such models used to rely on fixed word embeddings before the advent of transformers. Additionally, some models used in Computer Vision are yet to be tested in NLP applications. In this paper, we compare all these models, first adapting those made in the field of image processing to NLP, and second providing them access to transformers. We then test these models equipped with the same transformer-based encoder on the intent detection task, known for having a large number of classes. Our results reveal that while methods perform almost equally on the ARSC dataset, this is not the case for the Intent Detection task, where the most recent and supposedly best competitors perform worse than older and simpler ones (while all are given access to transformers).
Introduction Text classification often requires a large number of mappings between texts and target classes, so that it is challenging to build few-shot text classification models  (Geng et al. , 2019). With the recent advances of transformer-based models  (Devlin et al. , 2018; Wolf et al. , 2019)  along with their fine-tuning techniques  (Sun et al. , 2019) , text classification has significantly improved. In few-shot settings, methods based on these extracted text representations have been historically made of semi-supervision, especially thanks to pseudo-labeling  (Blum and  1 https://github. com/tdopierre/FewShotText Mitchell, 1998; Mihalcea, 2004; Zhi-Hua Zhou and Ming Li, 2005) , which aims at propagating known labels to unlabeled data points in the representational space. Such methods depend on the number of collected unlabeled data, which can also be costly to obtain  (Charoenphakdee et al.
, 2019) , and also suffer from the infamous pipeline effect in NLP  (Tenney et al. , 2019) , as cascade processing tends to make errors accumulate. In order to address the hindrance of collecting unlabeled data, modern approaches include unsupervised data augmentation techniques  (Xie et al. , 2019). It consists of generating samples through well-established text augmentation techniques in Neural Machine Translation, such as backtranslation  (Sennrich et al. , 2015; Edunov et al. , 2018) , and then use a consistency loss, training the classifier to assign the same prediction to all variations of the same sample text. While collecting new pseudo-labels can therefore be overcome by manipulating the dataset (especially using data augmentation techniques), the pipelining error accumulation effect instead calls for new neural architectures supporting scarcity of labeled data in an end-to-end fashion.
3). From that point-of-view, it is hardly possible to conclude if the improvement over time in few-shot text classification is due to new few-shot learning techniques or plainly to the significant advances made by text feature extractors. The same applies to vectors metrics: one method can use the cosine and another the euclidean distance, and that choice alone can impact conclusions made on the method being the state-of-the-art, although it could well rely only on the metric at work. Ultimately, experimental setups are usually restricted to one dataset, and evaluation schemes are heterogeneous among papers  (Yu et al. , 2018a) , • We revise different end-to-end neural architectures for few-shot text classification using the same transformer-based feature extractor, • We investigate how these re-implemented state-of-the-art solutions compete with very simple baselines found to be yet very competitive for few-shot classification in the field of image-processing, • We introduce an evaluation framework based on a number of intent detection datasets which is significantly bigger than what is usually used as evaluation in seminal papers transposing each of these architectures from image to text classification, • The entire framework used in this paper, including all the re-implemented methods plugged with up-to-date transformers, is provided as an open-source repository for further research.
Induction Networks Induction Networks  (Geng et al. , 2019)  aims at finding a general representation of each class in the support set to compare to new queries. They are composed of both an induction module and a relation module. The main motivation for such networks is that representing the class by the average vector of its data points -what is done in Prototypical and Relation networks -is too restrictive. The first part, the induction module, leverages a dynamic routing  (Sabour et al. , 2017)  algorithm. In their contribution,  Geng et al. (2019)  show that their method can better induce (hence their name) and generalize class-wise representations. For the second part, an NTL Relation Module is used: this is the same as the one introduced earlier (Section 2. 4). Such networks are illustrated in Figure  1d.
3 -how to produce good enough embeddings for the downstream classifier to learn efficiently. At test time, the same process is usedusing test labels -, except that we freeze the encoder's weights and only fine-tune the classifier part. The baselines architectures are represented in Figure  2. At testing time, the same sampling strategy is made, this time drawing classes among C test , with C test ∩ C train = ∅. The model is then evaluated on its ability to predict labels for the Q query samples, using the K support samples (unless otherwise stated, C, Q, and K values are the same at both testing and training time). This training procedure is called C-way K-shot classification. In all our experiments, we used K = Q = 5. Concerning the value of C, it is fixed to 2 for ARSC, as this dataset is already composed of binary classification tasks.