Improvement In Quality Of Response Research Articles

Evaluation of a context-aware chatbot using retrieval-augmented generation for answering clinical questions on medication-related osteonecrosis of the jaw.

The potential of large language models (LLMs) in medical applications is significant, and Retrieval-augmented generation (RAG) can address the weaknesses of these models in terms of data transparency and scientific accuracy by incorporating current scientific knowledge into responses. In this study, RAG and GPT-4 by OpenAI were applied to develop GuideGPT, a context aware chatbot integrated with a knowledge database from 449 scientific publications designed to provide answers on the prevention, diagnosis, and treatment of medication-related osteonecrosis of the jaw (MRONJ). A comparison was made with a generic LLM ("PureGPT") across 30 MRONJ-related questions. Ten international experts in MRONJ evaluated the responses based on content, language, scientific explanation, and agreement using 5-point Likert scales. Statistical analysis using the Mann-Whitney U test showed significantly better ratings for GuideGPT than PureGPT regarding content (p=0.006), scientific explanation (p=0.032), and agreement (p=0.008), though not for language (p=0.407). Thus, this study demonstrates RAG to be a promising tool to improve response quality and reliability of LLMs by incorporating domain-specific knowledge. This approach addresses the limitations of generic chatbots and can provide traceable and up-to-date responses essential for clinical practice.

Leveraging Retrieval-Augmented Generation for Swahili Language Conversation Systems

A conversational system is an artificial intelligence application designed to interact with users in natural language, providing accurate and contextually relevant responses. Building such systems for low-resource languages like Swahili presents significant challenges due to the limited availability of large-scale training datasets. This paper proposes a Retrieval-Augmented Generation-based system to address these challenges and improve the quality of Swahili conversational AI. The system leverages fine-tuning, where models are trained on available Swahili data, combined with external knowledge retrieval to enhance response accuracy and fluency. Four models—mT5, GPT-2, mBART, and GPT-Neo—were evaluated using metrics such as BLEU, METEOR, Query Performance, and inference time. Results show that Retrieval-Augmented Generation consistently outperforms fine-tuning alone, particularly in generating detailed and contextually appropriate responses. Among the tested models, mT5 with Retrieval-Augmented Generation demonstrated the best performance, achieving a BLEU score of 56.88%, a METEOR score of 72.72%, and a Query Performance score of 84.34%, while maintaining relevance and fluency. Although Retrieval-Augmented Generation introduces slightly longer response times, its ability to significantly improve response quality makes it an effective approach for Swahili conversational systems. This study highlights the potential of Retrieval-Augmented Generation to advance conversational AI for Swahili and other low-resource languages, with future work focusing on optimizing efficiency and exploring multilingual applications.

Enhancing quality in online surveys: uncovering foundational themes and strategies

Purpose: This study aimed to identify the foundational themes and strategies to enhance response quality in online surveys. Method: The term "online survey" and its variations were employed as broad keywords for the article selection process to identify methodological and empirical articles about online survey quality. The selected database was characterized using bibliometric techniques, the foundational themes were identified through co-citation analysis, and recommended strategies were determined using bibliographic coupling analysis. Findings: The foundational themes in online survey literature encompass Device, Mode of Administration, Question Design, Careless Responses, Response Rate, Paradata, Statistical Adjustment, Incentives, and Household Survey. Device and Mode of Administration emphasize the description and comparison of online collection methods with traditional approaches and explore the use of different devices. The remaining themes investigate strategies aimed at enhancing online survey response, focusing on specific strategies, quality indicators, or participant behaviors. Originality/Value: This study serves as a valuable guideline for survey researchers. To the authors’ knowledge, this is the first review using co-citation analysis to identify the main strategies for improving response quality in online survey research. Theoretical/Methodological contributions: This research contributes across many disciplines by uncovering the main strategies to improve response quality in online survey research and providing guidance for survey researchers. Social/Managerial contributions: By highlighting the importance of survey protocols and the potential biases and errors associated with unplanned research, the findings offer practical insights for social and managerial contexts.

RDSC: Range-Based Device Spatial Clustering for IoT Networks.

The growth of the Internet of Things (IoT) has become a crucial area of modern research. While the increasing number of IoT devices has driven significant advancements, it has also introduced several challenges, such as data storage, data privacy, communication protocols, complex network topologies, and IoT device management. In essence, the management of IoT devices is becoming more and more challenging, especially with the limited capacity and power of the IoT devices. The devices, having limited capacities, cannot store the information of the entire environment at once. In addition, device power consumption can affect network performance and stability. The devices' sensing areas with device grouping and management can simplify further networking tasks and improve response quality with data aggregation and correction techniques. In fact, most research papers are looking forward to expanding network lifetimes by relying on devices with high power capabilities. This paper proposes a device spatial clustering technique that covers crucial challenges in IoT. Our approach groups the dispersed devices to create clusters of connected devices while considering their coverage, their storage capacities, and their power. A new clustering protocol alongside a new clustering algorithm is introduced, resolving the aforementioned challenges. Moreover, a technique for non-sensed area extraction is presented. The efficiency of the proposed approach has been evaluated with extensive experiments that gave notable results. Our technique was also compared with other clustering algorithms, showing the different results of these algorithms.

SelfCP: Compressing over-limit prompt via the frozen large language model itself

Long prompt leads to huge hardware costs when using transformer-based Large Language Models (LLMs). Unfortunately, many tasks, such as summarization, inevitably introduce long documents, and the wide application of in-context learning easily makes the prompt length explode. This paper proposes a Self-Compressor (SelfCP), which adopts the target LLM itself to compress over-limit prompts into dense vectors on top of a sequence of learnable embeddings (memory tags) while keeping the allowed prompts unmodified. Dense vectors are then projected into memory tokens via a learnable connector, allowing the same LLM to understand them. The connector and the memory tag are supervised-tuned under the language modeling objective of the LLM on relatively long texts selected from publicly accessed datasets involving an instruction dataset to make SelfCP respond to various prompts, while the target LLM keeps frozen during training. We build the lightweight SelfCP upon 2 different backbones with merely 17M learnable parameters originating from the connector and a learnable embedding. Evaluation on both English and Chinese benchmarks demonstrate that SelfCP effectively substitutes 12× over-limit prompts with memory tokens to reduce memory costs and booster inference throughputs, yet improving response quality. The outstanding performance brings an efficient solution for LLMs to tackle long prompts without training LLMs from scratch.

Cyber Chatter

Abstract: Cyber Chatter, a web-based application built on the MERN stack, uses the power of GPT (Generative Pre-trained Transformer) technology to provide basic educational chat services and staff Building and ensure efficiency and they have been able to do it well. Cyber Chatter uses GPT-3.5, an advanced generic bid process, to provide accurate and meaningful answers to customer questions. With prior training in a large body of content knowledge, this demonstration demonstrates the incredible ability to develop and generate human-like emotions The application uses machine learning techniques to analyze customer data and continues to improve response quality over time. The use of Cyber Chatter has expanded to include Content Summarizer, Section Generator, Chat with Chatbot, Sci-fi image generator. Understudies can post questions, get answers, and engage in interesting conversations by using its natural user interface with GPT -a chatbot, Cyber Chatter. Cyber Chatter not only covers educational topics but also includes such features as content generators and JavaScript converters. Combining innovative features and an easy-to-use system, Cyber Chatter turns into a versatile punch where students can explore educational tools and interact with intelligent chatbots on a regular basis has enriched their learning experience

The way you assess matters: User interaction design of survey chatbots for mental health

The global pandemic has pushed human society into a mental health crisis, prompting the development of various chatbots to supplement the limited mental health workforce. Several organizations have employed mental health survey chatbots for public mental status assessments. These survey chatbots typically ask closed-ended questions (Closed-EQs) to assess specific psychological issues like anxiety, depression, and loneliness, followed by open-ended questions (Open-EQs) for deeper insights. While Open-EQs are naturally presented conversationally in a survey chatbot, Closed-EQs can be delivered as embedded forms or within conversations, with the length of the questionnaire varying according to the psychological assessment. This study investigates how the interaction style of Closed-EQs and the questionnaire length affect user perceptions regarding survey credibility, enjoyment, and self-awareness, as well as their responses to Open-EQs in terms of quality and self-disclosure in a survey chatbot. We conducted a 2 (interaction style: form-based vs. conversation-based) × 3 (questionnaire length: short vs. middle vs. long) between-subjects study (N=213) with a loneliness survey chatbot. The results indicate that the form-based interaction significantly enhances the perceived credibility of the assessment, thereby improving response quality and self-disclosure in subsequent Open-EQs and fostering self-awareness. We discuss our findings for the interaction design of psychological assessment in a survey chatbot for mental health.

The Advance of Multi-Round Dialogue System with Deep Learning

Multi-turn dialog systems have seen significant advances in recent years, driven by various approaches. The Multi-layer Semantic Method, Reinforcement Learning Method, Knowledge Graph Method, and Medicine Knowledge Graph Method have all shown promising results in advancing the state-of-the-art in this field. However, challenges remain in developing models that can handle complex and diverse user inputs and generate responses that are not only informative but also engaging and natural. Further research is needed to address these challenges and advance state of art in multi-turn dialogue systems. This paper reviews four critical methods for improving the quality of multi-turn dialogue systems: Multi-layer Semantic Method, Reinforcement Learning Method, Knowledge Graph Method, and Medicine Knowledge Graph Method. The Multi-layer Semantic Method utilizes multi-layer neural networks to model dialogue context and generate responses with improved coherence and relevance. The reinforcement Learning Method employs a reward-based approach to optimize response generation by training models to maximize long-term dialogue success. The knowledge Graph Method incorporates external knowledge sources, such as knowledge graphs, to enrich the dialogue context and improve response quality. The Medicine Knowledge Graph Method focuses on integrating medical knowledge into dialogue systems for healthcare applications. Each of these methods has demonstrated promising results in enhancing the quality of multi-turn dialogue systems.

Visual Design and Cognition in List-Style Open-Ended Questions in Web Probing

Previous research reveals that the visual design of open-ended questions should match the response task so that respondents can infer the expected response format. Based on a web survey including specific probes in a list-style open-ended question format, we experimentally tested the effects of varying numbers of answer boxes on several indicators of response quality. Our results showed that using multiple small answer boxes instead of one large box had a positive impact on the number and variety of themes mentioned, as well as on the conciseness of responses to specific probes. We found no effect on the relevance of themes and the risk of item non-response. Based on our findings, we recommend using multiple small answer boxes instead of one large box to convey the expected response format and improve response quality in specific probes. This study makes a valuable contribution to the field of web probing, extends the concept of response quality in list-style open-ended questions, and provides a deeper understanding of how visual design features affect cognitive response processes in web surveys.

CARE: Commonsense-Aware Emotional Response Generation with Latent Concepts

Rationality and emotion are two fundamental elements of humans. Endowing agents with rationality and emotion has been one of the major milestones in AI. However, in the field of conversational AI, most existing models only specialize in one aspect and neglect the other, which often leads to dull or unrelated responses. In this paper, we hypothesize that combining rationality and emotion into conversational agents can improve response quality. To test the hypothesis, we focus on one fundamental aspect of rationality, i.e., commonsense, and propose CARE, a novel model for commonsense-aware emotional response generation. Specifically, we first propose a framework to learn and construct commonsense-aware emotional latent concepts of the response given an input message and a desired emotion. We then propose three methods to collaboratively incorporate the latent concepts into response generation. Experimental results on two large-scale datasets support our hypothesis and show that our model can produce more accurate and commonsense-aware emotional responses and achieve better human ratings than state-of-the-art models that only specialize in one aspect.

Update of a Phase II Study of Lenalidomide-Elotuzumab As Maintenance Therapy Post-Autologous Stem Cell Transplant (AuSCT) in Patients (Pts) with Multiple Myeloma (MM)

Update of a Phase II Study of Lenalidomide-Elotuzumab As Maintenance Therapy Post-Autologous Stem Cell Transplant (AuSCT) in Patients (Pts) with Multiple Myeloma (MM)

The Benefits of Conversational Interviewing Are Independent of Who Asks the Questions or the Types of Questions They Ask

By clarifying the meaning of survey questions, interviewers help assure that respondents and researchers interpret questions the same way. This practice is at the heart of conversational interviewing and has been shown to improve response accuracy relative to standardized interviewing. This research investigates two issues: (1) Does conversational interviewing lead to improved response quality for opinion questions as it does for factual questions? and (2) Are some interviewers better suited to conduct conversational interviews than others? 490 respondents in the University of Michigan Surveys of Consumers participated in standardized telephone interviews after which they were re-asked five factual and five opinion questions. These questions were re-administered in conversational interviews for half the respondents; for the remaining half they were re-administered in standardized interviews. Interviewers also completed a nonverbal sensitivity questionnaire. Using response change between the two administrations of each question to measure response quality, the conversational technique improved quality while increasing interview duration. The comprehension benefits of conversational interviewing were no greater for opinion than factual questions. Moreover, interviewers low in nonverbal sensitivity more often gave definitions before respondents were able to speak, but this did not affect data quality (response change). Taken togetherthese results suggest that conversational interviewing can beeffectively administered by a range of professional interviewers,although those who are more attuned to respondents' comprehensionwill be more efficient, and the technique will equally benefit thequality of responses to questions about objective and subjectivephenomena.

Evaluating Writing Process Features in an Adult EFL Writing Assessment Context: A Keystroke Logging Study

ABSTRACT Keystroke logs provide a comprehensive record of observable writing processes. Previous studies examining the keystroke logs of young L1 English writers performing experimental writing tasks have identified writing processes features predictive of the quality of responses. Contrarily, large-scale studies on the dynamic and temporal nature of L2 writing process are scarce, especially in an assessment setting. This study utilized the keystroke logs of adult English as a foreign language (EFL) learners responding to assessment tasks to examine the usefulness of the process features in this new context. We evaluated the features in terms of stability, explored factor structures for their correlations, and constructed models to predict response quality. The results showed that most of the process features were stable and that their correlations could be efficiently represented with a five-factor structure. Moreover, we observed improved response quality prediction over a baseline by up to 48%. These findings have implications for the evaluation and understanding of writing process features and for the substantive understanding of writing processes under assessment conditions.

Data quality comparison between computers and smartphones in different web survey modes and question formats

PurposeLow response rates in web surveys and the use of different devices in entering web survey responses are the two main challenges to response quality of web surveys. The purpose of this study is to compare the effects of using interviewers to recruit participants in computer-assisted self-administered interviews (CASI) vs computer-assisted personal interviews (CAPI) and smartphones vs computers on participation rate and web survey response quality.Design/methodology/approachTwo field experiments using two similar media use studies on US college students were conducted to compare response quality in different survey modes and response devices.FindingsResponse quality of computer entry was better than smartphone entry in both studies for open-ended and closed-ended question formats. Device effect was only significant on overall completion rate when interviewers were present.Practical implicationsSurvey researchers are given guidance how to conduct online surveys using different devices and choice of question format to maximize survey response quality. The benefits and limitations of using an interviewer to recruit participants and smartphones as web survey response devices are discussed.Social implicationsIt shows how computer-assisted self-interviews and smartphones can improve response quality and participation for underprivileged groups.Originality/valueThis is the first study to compare response quality in different question formats between CASI, e-mailed delivered online surveys and CAPI. It demonstrates the importance of human factor in creating sense of obligation to improve response quality.

The utility of ground rule instructions with younger and older adult witnesses

SummaryThis study examined the effectiveness of ground rules—simple instructions outlining the communication expectations of an investigative interview—with 73 younger (age 18–40) and 57 older (age 60+) adults. Participants watched a film depicting an implied sexual assault and were interviewed after a brief delay. One third received no ground rules, one third received ground rules as statements, and one third received the statements along with practice examples. The interview contained questions that required participants to apply one of the rules. Those who received ground rules were asked their perceptions of the rules. Results demonstrated that practicing ground rules improved response quality to problematic recognition questions, younger adults showed more variability in responses to different ground rules than older adults, and most participants found ground rule instructions useful in investigative interviews. Results provide support for the use of ground rules in interviews with adult witnesses.

Allogeneic Hematopoietic Stem Cell Transplantation for T Cell Lymphomas: Improved Results Overtime

INTRODUCTION: Allogeneic hematopoietic stem cell transplantation (alloSCT) is the only curative strategy for relapsed/refractory T cell lymphoma (T-NHL). In the past ten years, there have been several improvements in conditioning regimens and graft versus host disease prophylaxis (GVHD), which have contributed to lower transplant-related mortality (TRM). Also, selective and low toxicity therapies, might improve response quality in some T-NHL Recently, haploidentical stem cell transplantation (Haplo) with post-transplant cyclophosphamide is a new option for those patients who do not have an HLA-identical sibling or a suitable unrelated donor, but also it has shortened the time for urgent cases. METHODS: This study analyzes overall outcomes of 211 consecutive patients diagnosed with T-NHL who received an alloSCT from 1995 to 2018 in GELTAMO/GETH centers. Previous therapies (chemotherapies and autologous stem cell transplantation) and baseline diagnostic parameters were recorded. RESULTS The median age at alloSCT was 47 years (range, 17-69). (see table 1). Forty-nine (23%) had primary extranodal disease. Disease status pre alloSCT was available in 202 patientes: 54% were in complete response (CR), 30% in partial response (PR) and 16% with stable/progressive disease (PD). Since 2013 BV was used as a bridge therapy in ≥ 3rd line in 25 patients with CD30+ tumor expression, it was effective in 20 (CR 68% (n=17), PR 12% (n=3) PD 16% (n=4), not assessed in 1 case). The use of BV was not associated with a better response probability pre alloSCT compared with other regimens used after third line and it did not impact on post alloSCT outcomes. Reduced intensity conditioning (RIC) was the most frequent (76%, n=156). (see table 2) GVHD prophylaxis were Methotrexate + CsaA or Tacrolimus (n=72, 35,8%), sirolimus-tacrolimus (n=37; 18,4%), Cy-post based (n=44, 21,9%; used in Haplo setting n= 29). The median follow-up of all cohort was 22.5 months (range, 0-280). The two year overall survival (OS) and disease free survival (DFS) were 60% (CI95%, 53-67%) and 76.7% (CI95%, 69.3-82.5%) (Figure 1A) We observed a significant improvement in alloSCT outcomes since 2011 (OS <2011 51.4% vs ≥2011 64.8%, p=0,04).(Figure 1B) Disease status was the only pre alloSCT variable that impacts 2 years - OS: CR 72.8% (CI95%, 63-80.4%), PR 52%(38.7-63.7%), PD 43.8 (26.5-59.8%) (p=0.002) (Figure 1C). Forty-three (21%) cases relapsed after alloSCT. To analyze the impact of GVHD on OS and DFS we selected landmark time point at day +100 and +1 year after alloSCT for acute (aGVHD) and chronic GVHD (cGVHD) respectively, which allowed us to capture the majority of events that could interfere with the analysis. A landmark analysis (day +100) showed a 2 year OS for grade 3-4 aGVHD was 18% and for 1-2 aGVHD 54,6% (p<0,001). The severity of aGVHD had no impact on DFS. Different grades of cGVHD did not impact OS nor DFS significantly. Cumulative incidence of acute GVHD at 90 days was 51.6% (CI95%, 43.9-58.2%) being 27% grade 3-4. Chronic GVHD at 6 months was 53.9% (46.1-60.5), 54% of cases were grade 3-4). The 2 years non relapse mortality (NRM) was 30.2% (CI95%, 23.3-36.5%); the main causes contributing to NRM were GVHD (40%) and infections (44%) Haploidentical (Haplo) alloSCT was introduced in 2012 (29 of 128). With a median follow up of 13 months (range, 0-60) we found that outcomes in terms of 1 year OS (Haplo 60.7% vs. others 67,5%), 1 year DFS (Haplo 74.8% vs. others 83.8%) and 1 year NRM (Haplo 29.7% vs. 26%) are similar to other alloSCT modalities (Figure 1D). Not additional analysis could be estimated due to the low number of population at risk for each category. CONCLUSION Overall outcomes of alloSCT for T-NHL have improved over time. Complete response pre alloSCT is the only determinant for OS. Haploidentical alloSCT is not significantly different from other approaches and should be considered as an alternative. Disclosures Sierra: Novartis: Honoraria, Research Funding, Speakers Bureau; Astellas: Honoraria; Pfizer: Honoraria; Daiichi-Sankyo: Honoraria, Speakers Bureau; Abbvie: Honoraria, Speakers Bureau; Roche: Honoraria; Jazz Pharmaceuticals: Honoraria.

Designing an Experience Sampling Method for Smartphone Based Emotion Detection

Smartphones provide the capability to perform in-situ sampling of human behavior using Experience Sampling Method (ESM). Designing an ESM schedule involves probing the user repeatedly at suitable moments to collect self-reports. Timely probe generation to collect high fidelity user responses while keeping probing rate low is challenging. In mobile-based ESM, timeliness of the probe is also impacted by user's availability to respond to self-report request. Thus, a good ESM design must consider - <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">probing frequency</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">timely self-report collection</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">notifying at opportune moment</i> to ensure high <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">response quality</i> . We propose a two-phase ESM design, where the first phase (a) balances between probing frequency and self-report timeliness, and (b) in parallel, constructs a predictive model to identify opportune probing moments. The second phase uses this model to further improve response quality by eliminating inopportune probes. We use typing-based emotion detection in smartphone as a case study to validate proposed ESM design. Our results demonstrate that it reduces probing rate by 64 percent, samples self-reports timely by reducing elapsed time between self-report collection, and event trigger by 9 percent while detecting inopportune moments with an average accuracy of 89 percent. These design choices improve the response quality, as manifested by 96 percent valid response collection and a maximum improvement of 24 percent in emotion classification accuracy.

Comparison among methods for improving response quality of surveys

Comparison among methods for improving response quality of surveys

Updated Results of a Phase II Study of Lenalidomide-Elotuzumab As Maintenance Therapy Post-Autologous Stem Cell Transplant (AuSCT) in Patients (Pts) with Multiple Myeloma (MM)

Background: Lenalidomide (LEN) monotherapy has been effective in extending progression free survival (PFS) after myeloablative AuSCT in pts with MM. Elotuzumab (ELO), a humanized IgG1 immunostimulatory monoclonal antibody against signaling lymphocytic activation molecule F7 (SLAM F7), is FDA approved in combination with LEN and dexamethasone (DEX) for treatment of MM pts who have received 1-3 prior therapies. The objective of this phase 2 trial is to evaluate the efficacy and safety of adding ELO to LEN as maintenance therapy post-myeloablative AuSCT. We report updated results of the primary (PFS) and secondary (overall survival [OS] and toxicity) endpoints. Patients and Methods: Between 4/15/2015-1/27/2016, 27 evaluable pts were treated on 28 day cycles with ELO, 10 mg/kg iv weekly for cycles 1-2, q2weeks for cycles 3-6, then 20 mg/kg once monthly for cycles 7+. Pts enrolled after 1/28/2016 (n=57 pts) have received ELO, 10 mg/kg IV weekly for cycles 1-2, and 20 mg/kg on day 1 from cycle 3 until disease progression (PD). LEN has been dosed at 10 mg/day for cycles 1-3, with a dose increase to 15 mg/day at physician discretion starting with cycle 4, in the absence of non-hematologic toxicity > grade 1 and significant cytopenias (ANC The study9s primary endpoint is PFS, defined as time from AuSCT to PD or death (whichever occurs first), or time of last contact. Secondary objectives are best response, OS, incidence of second primary malignancies (SPMs) and adverse event (AE) profile. Total enrollment of 100 pts is planned. Patients are followed until death, withdrawal of consent or removal from study. Eligible pts received ≤2 lines of induction therapy, and are 60-210 days post-AuSCT. Results: Pts (n=84) have been treated for a median of 16 cycles (2-43). At study entry, 27 (32%) had complete response (CR), 36 (43%) had very good partial remission (VGPR), 20 (24%) had partial remission (PR) and 1 (2%) had minor remission (MR). Best response achieved to date on study is CR in 44 pts (52%), VGPR in 31 pts (37%) and PR in 9 pts (11%). For those who have converted to CR on study, median time to CR was 2 months. Of 22 pts in CR who have been tested for minimal residual disease (MRD) to date, 20 are negative by flow cytometry (minimum of 2 million cells evaluated). Three of 20 have converted from VGPR to MRD negativity while on study. With a median follow up of 23 months, 96% of pts (n=81) remain alive. Ten pts have had PD; of these, 6 had high risk cytogenetics. Two died of PD while receiving salvage therapy. One additional pt died on study in VGPR, after developing acute cerebral encephalopathy with refractory status epilepticus of unclear etiology. Four pts withdrew for personal reasons, 4 were removed at physician discretion (prolonged cytopenias [1], drug rash [1], worsening memory impairment [1], therapy related myelodysplastic syndrome (t-MDS) [1]), and 2 lost insurance coverage. Estimated 3 year PFS is 81%. High risk cytogenetics adversely affected PFS (p=0.02). Grade 3-4 Hematologic AEs (no. of pts) were: neutropenia 32% (27), febrile neutropenia 15% (13), thrombocytopenia 8% (7), and anemia 7% (6). Grade 3-4 non-Hematologic AEs (no. of pts): respiratory infections 17% (15), diarrhea 14% (12), fatigue 13% (11), other infections 8% (7), peripheral neuropathy 7% (6), myalgias 6% (5), nausea/vomiting 4% (3), dizziness 2% (2), memory impairment 2% (2), maculopapular rash 2% (2), edema 1% (1). SPMs include intra-epidermal adenocarcinoma of the neck (1), mucinous appendiceal neoplasm (1), t-MDS (1), prostate cancer (1), and melanoma (1). Renal cell carcinoma was diagnosed in 1 pt, 15 months after removal from study for PD. Conclusions: Lenalidomide-elotuzumab is a well-tolerated maintenance therapy during which 33% of 84 pts had improvement in quality of response while on therapy, including 20% who converted to CR. The number of pts experiencing improvement may be underestimated due to ELO interference with paraprotein measurement on electrophoretic studies. Additional follow up is required to determine if the improved quality of responses translate into improvements in PFS and OS. Available data supports conduct of a Phase 3 trial. Disclosures Thomas:Array Pharma: Research Funding; Amgen Inc: Research Funding; Bristol Myers Squibb Inc.: Research Funding; Acerta Pharma: Research Funding; Celgene: Research Funding. Shah:Karyopharm Therapeutics: Employment. Lee:Celgene: Consultancy, Membership on an entity9s Board of Directors or advisory committees; Adaptive Biotechnologies Corporation: Consultancy; Amgen: Consultancy, Membership on an entity9s Board of Directors or advisory committees; Chugai Biopharmaceuticals: Consultancy; Takeda Oncology: Consultancy, Membership on an entity9s Board of Directors or advisory committees; Kite Pharma: Consultancy, Membership on an entity9s Board of Directors or advisory committees. Patel:Abbvie: Research Funding; Celgene: Research Funding; Takeda: Research Funding; Poseida Therapeutics, Inc.: Research Funding.

Survey Development for Adolescents Aged 11–16 Years: A Developmental Science Based Guide

Methodological knowledge on surveying young adolescents is scarce and researchers often rely on theories and methodological studies based on adult respondents. However, young adolescents are in the process of developing their cognitive, psychological, emotional and social skills, therefore present a unique set of considerations. Question characteristics including; question type and format, question difficulty, wording, ambiguity, the number of response options, and the inclusion of a neutral mid-point, play a pivotal role in the response quality of young adolescents. Failure to address these factors is likely to encourage young adolescents to use satisficing techniques. In this article, we provide a science based guide for developing surveys for use with adolescents aged 11–16 years. The guide considers the characteristics and developmental stages of adolescents as survey responders and incorporates advice on appropriate question characteristics, survey layout and question sequence, approaches to pre-testing surveys and mode of survey administration. The guide provides recommendations for developmentally appropriate survey design to improve response quality in survey research with young adolescents.