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As digital media grows, there is an increasing demand for engaging content that can captivate audiences. Along with that, the monetary conversion of those engaging videos is also increased. This leads to the way for more content-driven videos, which can generate revenue. YouTube is the most popular platform which shared the revenue from advertisement to video publisher. This paper focuses on the work of video popularity prediction of the YouTube data. The idea of mapping the video features into low-dimensional space to get the latent features is presented. This mapping is achieved by a novel multi-branch child-parent Long Short Term Memory (LSTM) network. These latent features train the fused Convolutional Neural Network (CNN) with LSTM to predict the popularity of unseen videos on the trained deep learning network. We compared our results against Linear Regression (LR), Support Vector Regression (SVR) and Fully Convolutional Networks (FCN) with LSTM. A significant improvement with a 50% reduction in MAE and a 0.61% increase in the coefficient of determination (R²) has been observed by the proposed Multi branch LSTM encoded features with a fused deep learning predictor (MLEF-DL predictor) when compared to existing methods.

The creation and spreading of fake information can be carried out very easily through the internet community. This pervasive escalation of fake news and rumors has an extremely adverse effect on the nation and society. Detecting fake news on the social web is an emerging topic in research today. In this research, the authors review various characteristics of fake news and identify research gaps. In this research, the fake news dataset is modeled and tokenized by applying term frequency and inverse document frequency (TFIDF). Several machine-learning classification approaches are used to compute evaluation metrics. The authors proposed hybridizing SVMs and RF classification algorithms for improved accuracy, precision, recall, and F1-score. The authors also show the comparative analysis of different types of news categories using various machine-learning models and compare the performance of the hybrid RFSVM. Comparative studies of hybrid RFSVM with different algorithms such as Random Forest (RF), naïve Bayes (NB), SVMs, and XGBoost have shown better results of around 8% to 16% in terms of accuracy, precision, recall, and F1-score.

Time-to-event endpoints for patient-reported outcomes, such as time to deterioration of symptoms or function, are frequently used in cancer clinical trials. Although time-to-deterioration endpoints might seem familiar to cancer researchers for being similar to survival or disease-progression endpoints, there are unique considerations associated with their use. The complexity of time-to-deterioration endpoints should be weighed against the information that they add to the tumour, survival, and safety data used to inform the risks and benefits of an investigational drug. Here we use the estimand framework to show how analytical decisions answer different clinical questions of interest, some of which might be uninformative. Challenges including the consideration of intercurrent events, the difficulty in maintaining adequate completion rates, and considerable patient and trial burden from long-term, serial, patient-reported outcome measurements render time to deterioration a problematic approach for widespread use. For trials in which a comparative benefit in symptoms or function is an objective, an analysis at pre-specified relevant timepoints could be a better approach.

Patient-reported outcomes (PROs) are used in clinical trials to provide valuable evidence on the impact of disease and treatment on patients’ symptoms, function and quality of life. High-quality PRO data from trials can inform shared decision-making, regulatory and economic analyses and health policy. Recent evidence suggests the PRO content of past trial protocols was often incomplete or unclear, leading to research waste. To address this issue, international, consensus-based, PRO-specific guidelines were developed: the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT)-PRO Extension. The SPIRIT-PRO Extension is a 16-item checklist which aims to improve the content and quality of aspects of clinical trial protocols relating to PRO data collection to minimise research waste, and ultimately better inform patient-centred care. This SPIRIT-PRO explanation and elaboration (E&E) paper provides information to promote understanding and facilitate uptake of the recommended checklist items, including a comprehensive protocol template. For each SPIRIT-PRO item, we provide a detailed description, one or more examples from existing trial protocols and supporting empirical evidence of the item’s importance. We recommend this paper and protocol template be used alongside the SPIRIT 2013 and SPIRIT-PRO Extension paper to optimise the transparent development and review of trial protocols with PROs.

Patients and the wider public are beneficiaries of scientific research that leads to new drugs and medical technologies, but they can and should be able to contribute to these advances through participation in clinical studies, co-design of research and input into regulatory processes.Patients and the wider public are beneficiaries of scientific research that leads to new drugs and medical technologies, but they can and should be able to contribute to these advances through participation in clinical studies, co-design of research and input into regulatory processes.

Over the past 13 years, there have been advances in characterizing the patient experience in oncology trials, primarily using patient-reported outcomes (PROs). This review aims to provide details on the PRO measures and analyses used in multiple myeloma (MM) registrational trials. We identified registrational trials supporting MM indications from 2007 to 2020 from FDA databases. Trial protocols, statistical analysis plans, and clinical study reports were reviewed for PRO measures used, collection methods, statistical analyses, baseline and instrument completion definitions, and thresholds for clinical meaningfulness. Twenty-five trials supporting 20 MM indications were identified; 17 (68%) contained submitted PRO data. Of the 17 trials, 14 were randomized controlled trials and the remainder were single-arm trials. All but one trial were open label trials. Seven trials collected data electronically and five in paper format. The majority of trials evaluated at least two PRO measures (82%) with two trials (12%) utilizing four measures. Nine unique PRO measures were used, most commonly the EORTC QLQ-30 (87%), EQ-5D (65%), and QLQ-MY20 (47%). All 17 (100%) trials provided descriptive summaries, 10 (59%) carried out longitudinal mixed model analysis, 9 (53%) conducted responder analysis, and 2 (12%) did a basic inferential test. We noted substantial heterogeneity in terms of PRO collection methods, measures, definitions, and analyses, which may hinder the ability to effectively capture and interpret patient experience in future MM clinical trials. Further research is needed to determine the most appropriate approaches for statistical and analytical methodologies for PRO data in MM trials.

The use of item libraries for patient-reported outcome (PRO) measurement in oncology allows for the customisation of PRO assessment to measure key health-related quality of life concepts of relevance to the target population and intervention. However, no high-level recommendations exist to guide users on the design and implementation of these customised PRO measures (item lists) across different PRO measurement systems. To address this issue, a working group was set up, including international stakeholders (academic, independent, industry, health technology assessment, regulatory, and patient advocacy), with the goal of creating recommendations for the use of item libraries in oncology trials. A scoping review was carried out to identify relevant publications and highlight any gaps. Stakeholders commented on the available guidance for each research question, proposed recommendations on how to address gaps in the literature, and came to an agreement using discussion-based methods. Nine primary research questions were identified that formed the scope and structure of the recommendations on how to select items and implement item lists created from item libraries. These recommendations address methods to drive item selection, plan the structure and analysis of item lists, and facilitate their use in conjunction with other measures. The findings resulted in high-level, instrument-agnostic recommendations on the use of item-library-derived item lists in oncology trials.

Background The US Food and Drug Administration (FDA) released a draft guidance document detailing core patient-reported outcomes in cancer clinical trials, including physical function (PF). The objectives of this study were to develop analytic methods and visualizations of patient-reported PF in patients with cancer. Methods We applied an estimand framework to a patient-reported tolerability endpoint to develop data summaries cross-sectionally and over time, along with visualizations. We accomplished this through iterative feedback with clinicians, statisticians, and FDA stakeholders using three clinical trial datasets in hematologic malignancies. Graphical approaches were applied to three datasets in hematologic malignancies: (1) patients with myeloproliferative neoplasms enrolled in MPN-RC 111/112 trials completed EORTC QLQ-C30 over 12 months; (2) patients with hematologic malignancies undergoing CAR-T cell therapy or autologous transplant who completed FACT questionnaires over 6 months; and (3) patients with multiple myeloma or amyloidosis who completed the PROMIS-29 questionnaire over 6 months. Zoom polls were administered to two stakeholder groups (clinicians/clinical investigators and patient advocates) to elicit feedback. Results Visualizations included stacked bar charts, line plots of arithmetic mean changes from baseline, pie charts, waffle plots, and waterfall plots of PF data. Graphics considered scaled scores and individual items and included delineation of PRO completion rate at each time point. Confidence intervals and reference lines were included as applicable, and colorblind accessible colors were implemented to ensure inclusivity of all visualizations. Data summaries over time reporting “worst” change were difficult to interpret. In terms of stakeholders’ preference, patients preferred stacked bar charts while clinicians equally favored stacked bar charts and line plots; both patients and clinicians preferred waterfall plots to pie charts. Patient feedback highlighted the need for various graphics to convey group level trends and granular individual-patient level information. Conclusion Patient-reported PF informs the evaluation of treatment tolerability in cancer trials. Data summaries and visualizations of physical function developed through an iterative process were reviewed favorably by patients, clinicians and FDA stakeholders in this study. Future work to systematically assess accuracy of interpretation of the various analytic and visualization methods is a necessary next step across clinical, regulatory, payer and patient stakeholders. Trial registration NCT01259817, NCT01259856.

Purpose On August 2, 2017, the Food and Drug Administration approved ibrutinib (IMBRUVICA) for the treatment of patients with chronic graft versus host disease (cGVHD) after the failure of one or more lines of systemic therapy. The approval was based on results from a single-arm, multicenter trial that enrolled patients with refractory cGVHD. This paper describes the FDA review of patient-reported outcomes (PRO) data from Study PCYC-1129-CA and the decision to incorporate descriptive PRO data in the FDA label to support the primary clinician-reported outcome results. Methods In this trial, the Lee Chronic GVHD Symptom Scale (LSS) was used to capture patient-reported symptom bother. The 42 patients who received treatment were included in the analysis and completed the PRO tool. Post hoc descriptive analyses were conducted to further understand the measurement properties of the LSS. Results The analysis submitted to FDA reported that 18 patients had a ≥ 7-point improvement on the LSS overall summary score at any point during the assessment period. For 10 patients, the ≥ 7-point improvement was sustained for ≥ 2 consecutive PRO assessments. An assessment of the responder threshold suggested the threshold submitted to the FDA was reasonable and in line with clinical findings. Conclusions Overall, study PCYC-1129-CA demonstrated favorable clinician-reported cGVHD efficacy results that were complemented by results from PRO data, supporting the FDA’s positive benefit-risk assessment leading to regular approval. Limitations included the single-arm trial design, responder definition, and instrument shortcomings. These limitations were thoroughly explored through additional FDA post hoc analyses.