dataesr/scientific-paragraphs-categorization huggingface dataset:
The WG members were intentionally selected to ensure diversity in scientific expertise, career stage and demographics while ensuring the inclusion of other stakeholders such as patient advocates and industry representatives. Final CDEs are expected to be published in 2022. The intended impact of this foundational work is to (1) reduce time/cost to develop data collection tools, (2) reduce study start-up time, (3) promote data collection in a consistent format, (4) improve data quality, and (5) facilitate collaborations and comparisons between studies and meta-analyses. Apart from CDE development, there is a need for a central database that would allow us to gain insights into mechanisms, predictors, and trajectory of recovery of coma and to lay the foundation for interventional trials. Currently, there are only a few limited data sets related to DoC with significant gaps, including variability in data collection and fragmentation of data between acute and long-term care.
Existing consortia  [47] [48] [49]  offer great experience but also have their own limitations in terms of data diversity and integration. The goal of the CCC coma science database is to be at the intersection of databases focused on (1) scientific discovery, (2) epidemiology, comparative effectiveness, and implementation science, and (3) clinical trials. Based on expert interviews and the needs assessment survey within the scientific community, a set of requirements for the coma science database has been developed. This database is envisioned to broadly capture the needs of all the CCC scientific WGs. A comprehensive coma science database should be able to  (1)  identify links between structural and functional abnormalities of DoC (2) integrate data across micro and macro scales (3) integrate theory-driven and data-driven approaches (4) facilitate the bridging of bench and clinical research related to DoC (5) facilitate coma research across the temporal spectrum from acute injury to longterm recovery (LTR), and (6) facilitate coma research across the lifespan from pediatric to adult patients.
Given the breadth of data required for this project, we need a robust database architecture. Therefore, an ideal database would include the ability to: The coma science database WG has envisioned a complex network of tiered data collection approaches to include small community centers with clinical data to the large academic centers with the ability to obtain advanced imaging and high-resolution physiological data. Additionally, the development of a longitudinal model system to collect data from acute to chronic phases of DoC and beyond would facilitate the conduct of robust outcome studies of DoC. Data from various sources are envisioned to be housed in designated core CCC database centers across the globe, where the data are federated, curated, and distributed to promote DoC research. The WG has identified 50 commercial, academic, and registry-based database enterprises and is actively engaged in conversations with eligible vendors to select the right database for this colossal project.
Research Priorities for Coma Science Database To support the development of a coma science database, the WG proposed the following seven priorities: 1. Develop a hybrid database architecture leveraging DoC-specific CDEs focused on linking high-resolution imaging and physiologic data with minimum necessary clinical data, including the long-term outcome data. 2. Design a tier-based network organization to maximize the scalability of high-impact science and clinical trial design. 3. Facilitate implementation of the broader research priorities of the CCC community, spanning scientific discovery and clinical evidence to clinical practice by focusing on concrete, short-term objectives. 4. Facilitate translational scientific discoveries through an infrastructure that enable novel hypothesis discovery and preclinical validation of potential therapies.
Panel Discussion of the Approach The panel agreed on the need to create a centralized prospective database structure to advance coma science and develop the foundation to support research priorities of other WGs. A comprehensive database accommodating acute through chronic phases of DoC is needed to cater to a variety of researchers across disease trajectory. Big data would be valuable regardless of whether it is collected at centers of excellence in a preplanned standardized format or heterogeneous clinical data acquired in a nonresearch setting, as it allows comparisons across cohorts and can help combine data-driven models of structure and function with theory-driven models. Additionally, big data are needed to test hypotheses, discover therapies and to develop personalized treatment of this complex disorder.
The panel discussed that challenges regarding data collation of existing databases involving DoC include the use of heterogeneous data definitions (e. g. , subjective vs. objective data), clinical practice variability, nonstandardized prognostication, and patient/family communication and the variability of management in different facilities across the trajectory of care. In terms of high-fidelity data, specific challenge includes the difference in data sampling frequency across institutions due to the lack of existing standards, thereby limiting data harmonization to the lowest sampling frequency across cohorts. The creation of CDEs and development of standardized protocols are therefore crucial to address these inconsistencies. Patient privacy regulation is another barrier when trying to collate data along disease trajectory from acute, subacute to chronic phases.
The panel agreed that innovative ways to address privacy issues are needed to overcome barriers across care settings. Further, apart from serving as the structural unit for centralized data collection, CCC will also serve as the center of excellence for DoC to promote education regarding standardized data definitions, standardized data collection approaches, standardized communication around DoC with patients and families, and the development of guidelines related to DoC. A comprehensive database approach initiative of the CCC is envisioned to bridge the silos across various stakeholders including preclinical researchers, clinical researchers, nonclinicians, patients, and families and also across the disease trajectory in any individual patient. The panel expressed that a spoke and hub model is likely appropriate for such a massive undertaking with designated cores or centers of excellence for various types of data such as clinical data, biomarkers, physiologic and the imaging data.
Efforts should be focused on developing models with accurate calibration using large amount of clinical data. There are several existing candidate tests and data which need to be evaluated simultaneously with an aim toward development of multivariable models to allow incremental accuracy of neuroprognostication. To develop such models, we need a large multicenter prospective observational coma registry that includes key prognostic data including diverse etiologies of DoC with serial testing over time. Important variables to consider include the following: (1) clinical examination including phenotypes of DoC (2) somatosensory evoked potential (SSEP) to assess thalamocortical connectivity, (3) serological/cerebrospinal fluid/chemical/genetic biomarkers, (4) Functional neuroimaging pertaining to stages of coma, (5) EEG with quantified parameters, and (6) brain-computer interface and other novel technologies.
9. Availability of resources and costs associated with long-term coma and DoC management for patients and families. The WG emphasized the need for a pragmatic approach to advance care of the patients with DoC with the appropriate understanding of global disparities in care and regional cultures and practice preferences. The group highlighted the need for close communication and collaboration between preclinical and clinical research teams to create a highly functional and cohesive research group. Data repositories should include current clinical practices across DoC etiology and across the spectrum of care so future research can appropriately account for these variations while actively standardizing practice where possible.
A model complex enough to identify which changes are significant and how they affect the patients does not yet exist. Large volume of prospective data with models including overt and covert clinical variables pertaining to routine clinical care are needed to improve the reliability of AI in critical care settings. Although care of the coma patients includes optimization and standardization of sophisticated measures such as electrophysiological, biophysical, and other biomarkers as well as innovation such as AI, approaching the low hanging fruits such as standardization of basic approach for evaluation of comatose patients can be impactful. In the current clinical setting, simple care aspects such as the variability in the basic assessment of the comatose patient (e. g. , pupillary assessment using a penlight, vs.
Biomarker discovery requires a phased approach with (1) designing data-driven models to learn from the existing databases of patients with DoC using big data and ML technology; (2) identification of potential new biomarkers and strategies to harness data-driven evidence to guide rehabilitation; and (3) developing a consensus for the routine use of the most informative long-term biomarkers. In summary, we need to develop valid and effective value-based care paradigms to, improve quality of care, increase access to care, prevent costly complications, detect changes in trajectories that require modifications of care, develop mechanisms to collect data, share data and have communication mechanisms in place to deliver collaborative care. Such a model would then allow us to provide the care that could help improve outcomes in patients with DoC.
Panel Discussion of the Approach The panel discussed that we currently lack meaningful data on patients with DoC from vulnerable populations with regards to age, gender, and ethnicity. There is a need to focus on a large-scale population study that includes vulnerable populations to adequately represent these groups into the data sets with derivation of relevant outcome measures. The panel agreed that various LTR priorities align with NIH priorities along the domains of longitudinal studies, translational biomarkers, integration of technology and models of multidisciplinary integrative care. Although DoC affects millions of patients worldwide and is a major source of morbidity and mortality with high economic impact, adequate funding is lacking in this field of research. Panel agreed that with appropriate funding, CCC is well-suited to spearhead this mission to not only leverage existing infrastructure such as the NCRC, global collaborative networks and Neurocritical Care Foundation, but also to develop a central platform for a wide group of clinicians, scientists and other stakeholders to synergistically work toward a cure for coma.
II) Existing needs to find a cure for comaThe main research needs for curing coma can be broadly categorized into three main components: a. Infrastructure: Includes database, clinical trial standards, CDEs and strategic collaboration. b. Basic research for cure: Includes understanding of the biology of coma, development of biomarkers for diagnosis and prognostication, preclinical model/animal studies and prospective studies. c. Care of the coma patient: This includes understanding of effectiveness and timing of current routine tests, treatments and interventions, evidence-based approach to reduce practice variability, research related to prognostication and LTR.
Database development: 2 million • Biology and new biomarker development: 15 million • Prospective studies: 10 million • Clinical trials for treatment evaluations: 13 million • Patient care protocol development: 10 million • Prognostication and ethics: 10 million Over the past 10 years, about 24. 3 million (average of 2. 4 million/year) NIH dollars have already been invested.
• Identification of new tools and technologies needed to cure coma. • Estimation of resources (e. g. , funding, database) required to cure coma. • Elimination of major barriers that hinder our ability to cure coma. • Risk assessment as well as the development of mitigation strategies. • Development of action plan with milestones. (III) Main prioritiesss of the CCC A. Improve patient care based on what we know and can do today to raise the standard of care and improve outcomes. This involves: 1. Capitalize on existing data sets. 2. Prospective multicenter interdisciplinary studies to define the science of caring for patients with DoC that creates the optimal context in which recovery can occur. 3. Develop CDEs/common language. 4. Analysis and communication of best practices. B. Understand coma at the neuronal level and defining endotypes to understand the nature and location of the defect and its potential outcome trajectory.
Development of large-scale, randomized, controlled trials 4. Utilization of existing tools while focusing on development of new tools, CDEs and biomarkers for standardization. F. Develop and implement a database that includes clinical, imaging, electrophysiological, chemical, epigenetic and other data. This will create a central repository of relevant DoC data for research collaborations and eventually the development of algorithms for real-time development of decision support tools at the bedside, G. Insist and ensure that every trial, technical thrust.
( II) Existing needs to find a cure for comaThe main research needs for curing coma can be broadly categorized into three main components: a. Infrastructure: Includes database, clinical trial standards, CDEs and strategic collaboration. b. Basic research for cure: Includes understanding of the biology of coma, development of biomarkers for diagnosis and prognostication, preclinical model/animal studies and prospective studies. c. Care of the coma patient: This includes understanding of effectiveness and timing of current routine tests, treatments and interventions, evidence-based approach to reduce practice variability, research related to prognostication and LTR.
• Database development: 2 million • Biology and new biomarker development: 15 million • Prospective studies: 10 million • Clinical trials for treatment evaluations: 13 million • Patient care protocol development: 10 million • Prognostication and ethics: 10 million Over the past 10 years, about 24. 3 million (average of 2. 4 million/year) NIH dollars have already been invested.
Develop- nity and family given the impact of caregivers and com-technology such as the virtual reality should be con- ment of prediction models would be helpful to guide munity on patient outcome; (3) technology development sidered. Cognitive training is equally important with clinical decisions. Inclusion of physiatry colleagues by promoting interdisciplinary collaboration within the the development of age and stage-appropriate con- in LTR research is critical. Several existing databases health disciplines and with colleagues in computer sci-tent for adequate cognitive rehab. Additionally, aug- and networks for adult recovery-based research on ence, math, statistics, engineering, and the end-users of mented communication tools using brain-computer various DoC etiologies such as TBI [89-92], CA the technology; (4) development of new methods and interface can significantly enhance quality of life of [93-95], and others [68] must be leveraged to expand designs for research with emphasis on data sharing and patients and caregivers and should also be prior- our understanding of LTR.
Such existing database knowledge translation; (5) promotion of translational sci-itized. As new innovative tools continue to develop, and registries could help construct robust generaliz- ence in rehabilitation research to advance understanding equality in access to care should be a key considera- able models to predict long-term outcomes, facili- of biological, physiological and behavioral mechanisms tion to ensure that these initiatives do not expand the tate studies with propensity score matching of DoC that underlie DoC recovery and adaptation through pre-existing gap in health care disparity [99]. The WG cohort with or without maximal medical therapy and cision medicine, animal models and genomics; and (6) suggested a tiered approach to technological devel- plan future multisite prospective observational stud- building research capacity and infrastructure through opment and innovation.
Such approach could include ies. training, consultation, and collaboration to develop basic assessing existing technological capacity and capabil- 3. Utilization of existing databases and networks for scientists, physician scientists, allied health profession-ity in phase 1, building collaborations across centers pediatric/Neonatal recovery-based research: Similar als, and engineers who focus on biomedical devices or with similar technology and rehabilitation protocols to the adult registries, leveraging existing pediatric rehabilitation. in phase 2 and ultimately designing new technology- and neonatal databases [96, 97] and establishment of These rehabilitation research priorities of NIH [88] based rehabilitation programs in phase 3. cross-collaboration across research groups is essen- align with many priorities of the LTR WG and fit within 5.
Development of large-scale, randomized, controlled trials 4. Utilization of existing tools while focusing on development of new tools, CDEs and biomarkers for standardization. F. Develop and implement a database that includes clinical, imaging, electrophysiological, chemical, epigenetic and other data. This will create a central repository of relevant DoC data for research collaborations and eventually the development of algorithms for real-time development of decision support tools at the bedside, G. Insist and ensure that every trial, technical thrust 1. Electromagnetic 2. Mechanical 3. Sensory 4. Chemical 5. Regenerative D.
Study design and patient population This was a post hoc analysis of the AVERT (NCT01142726, June 11, 2010) study  [17]. AVERT was a phase IIIb, randomized, active-controlled 24-month study in adult patients with early (≤2 years), active RA consisting of a 12-month double-blind treatment period and a subsequent treatment withdrawal period (see Supplementary Fig. 1  in Additional File 1). All patients in AVERT satisfied the 2010 American College of Rheumatology/European League Against Rheumatism classification criteria for RA  [17, 19]  and were anti-cyclic citrullinated peptide (anti-CCP) positive. Details of sample size, power considerations, and methods for primary and secondary analyses in AVERT have previously been reported  [17]. All patients who discontinued prior to completing the treatment or withdrawal period were imputed as non-responders for the month 12 or 18 analyses  [17].
Statistical analyses WD patient demographic and disease characteristics stratified by flare status at WD+6mo and WD+12mo were described. Differences between patient and disease characteristics at WD in patients with and without flare at WD+6mo and WD+12mo were estimated using a Student's t-test for equality of means (continuous variables) or a chi-square test (categorical variables). No correction for multiple testing was performed. Data from WD (or AVERT study baseline for age, weight, and duration of RA) were standardized to have a mean equal to zero and a standard deviation (SD) equal to one and were compared by the estimated differences between the flare versus no flare groups. P values of comparison were calculated by performing a Student's t test. To assess the relationship of disease characteristics and MRI scores at WD with flare status at WD+6mo and WD+12mo, data from WD were standardized to have a mean equal to zero and an SD equal to one.
As more data on predictors of flare after treatment taper or withdrawal are collected, Fig. 3  Multivariable logistic regression analysis assessing the value of cut-off scores for predicting flare. Analysis was performed for flare status at WD+6mo and WD+12mo. A multivariable logistic regression model, adjusted for treatment arm, determined whether patient-reported outcome (PRO) and MRI measures at WD were independent predictors of flare at WD+6mo and WD+12mo. P values in bold type indicate statistical significance. Vertical line indicates limit of effect: positive data indicate effect, negative data or data that cross 1 indicate absence of effect. *Synovitis score + (2x bone edema score). MTX methotrexate, SC subcutaneous. See Fig. 1  for other definitions a combination of clinical and imaging factors may be defined for the accurate identification of patients suitable for treatment withdrawal or those who would be at risk of flare.
In-Depth Interviews We used purposeful sampling based on professional status (physicians/nurses) and number of cDCD experiences  (23). Participants were recruited through e-mail and personal solicitations. Interviews were conducted individually and in-person by a single investigator (SM) and lasted between 1 and 2 h. All interviews were audio recorded, pseudonymized, and then transcribed verbatim for analysis. Data collection was interrupted when we reached data saturation, namely when no new themes emerged from the interviews  (24).
a ILD/pneumonitis was adjudicated by an independent AC and was graded using the CTCAE version 5. 0 for pneumonitis. b For DESTINY-Breast01, the ORR and ILD/pneumonitis data were from the March 2021 data cutoff; the most common grade ≥ 3 adverse events were from the primary data cutoff of August 1, 2019. c ORR was assessed by blinded ICR. d This category includes the preferred terms neutrophil count decreased and neutropenia. e This category includes platelet count decreased and thrombocytopenia. f This category includes the preferred terms white blood cell count decreased and leukopenia. g These adverse events were the most frequent grade ≥ 3 drug-related treatment-emergent adverse events among drug-related treatment-emergent adverse events with ≥ 20% overall (ie, all grades combined) incidence.
Preclinical data In a cynomolgus monkey study, diffuse lymphocytic infiltrates and The Asian-Japan race-country covariate group was identified by an exposure-safety analysis that used a Cox proportional hazards regression model; ILD grade ≥ specifically was not found to be associated with any covariates; all-grade ILD was associated with Asian-Japan race-country covariate group. Covariates in this analysis included race (Asian vs White vs other) and country (Japan vs non-Japan), which were correlated and combined into a single race/country covariate. T-DXd dose was identified by a dose-response projection analysis that used the model developed in the exposure-response analysis. slight fibrosis were observed in T-DXd-related ILD/pneumonitis  [40]. The inflammation and fibrosis in monkeys were similar to ILD/pneumonitis pathology patterns seen in humans, suggesting that these same mechanisms could play a role in humans  [40].
In patients with symptoms of ILD/pneumonitis, reduced oxygen saturation, or other suspicion of ILD/ pneumonitis, we recommend performing (1) pulmonary function testing with spirometry for forced vital capacity (FVC) and (2) diffusing capacity, and (3) an exertional pulse oximetry walk test or six-minute walk test at each regularly scheduled visit following the trend of the FVC, diffusing capacity for carbon monoxide, and lowest saturation on the walk test. These tests should also be performed at visits where shortness of breath is being assessed. No data are available regarding the use of pulse oximetry in T-DXd-related ILD/pneumonitis; therefore, future research is needed to understand its usefulness. Recently, a modified Delphi panel with three rounds of data collection (two anonymous online surveys [questionnaires with agree/disagree ratings] and one virtual meeting to facilitate expert discussion) was conducted to obtain expert clinicians' opinions and recommendations for a functional definition of ILD as part of a larger study.