Leveraging artificial intelligence to summarize abstracts in lay language for increasing research accessibility and transparency.

OBJECTIVE: Returning aggregate study results is an important ethical responsibility to promote trust and inform decision making, but the practice of providing results to a lay audience is not widely adopted. Barriers include significant cost and time required to develop lay summaries and scarce infrastructure necessary for returning them to the public. Our study aims to generate, evaluate, and implement ChatGPT 4 lay summaries of scientific abstracts on a national clinical study recruitment platform, ResearchMatch, to facilitate timely and cost-effective return of study results at scale. MATERIALS AND METHODS: We engineered prompts to summarize abstracts at a literacy level accessible to the public, prioritizing succinctness, clarity, and practical relevance. Researchers and volunteers assessed ChatGPT-generated lay summaries across five dimensions: accuracy, relevance, accessibility, transparency, and harmfulness. We used precision analysis and adaptive random sampling to determine the optimal number of summaries for evaluation, ensuring high statistical precision. RESULTS: ChatGPT achieved 95.9% (95% CI, 92.1-97.9) accuracy and 96.2% (92.4-98.1) relevance across 192 summary sentences from 33 abstracts based on researcher review. 85.3% (69.9-93.6) of 34 volunteers perceived ChatGPT-generated summaries as more accessible and 73.5% (56.9-85.4) more transparent than the original abstract. None of the summaries were deemed harmful. We expanded ResearchMatch's technical infrastructure to automatically generate and display lay summaries for over 750 published studies that resulted from the platform's recruitment mechanism. DISCUSSION AND CONCLUSION: Implementing AI-generated lay summaries on ResearchMatch demonstrates the potential of a scalable framework generalizable to broader platforms for enhancing research accessibility and transparency.

A multi-stage transfer learning strategy for diagnosing a class of rare laryngeal movement disorders.

BACKGROUND: It remains hard to directly apply deep learning-based methods to assist diagnosing essential tremor of voice (ETV) and abductor and adductor spasmodic dysphonia (ABSD and ADSD). One of the main challenges is that, as a class of rare laryngeal movement disorders (LMDs), there are limited available databases to be investigated. Another worthy explored research question is which above sub-disorder benefits most from diagnosis based on sustained phonations. The question is from the fact that sustained phonations can help detect pathological voice from healthy voice. METHOD: A transfer learning strategy is developed for LMD diagnosis with limited data, which consists of three fundamental parts. (1) An extra vocally healthy database from the International Dialects of English Archive (IDEA) is employed to pre-train a convolutional autoencoder. (2) The transferred proportion of the pre-trained encoder is explored. And its impact on LMD diagnosis is also evaluated, yielding a two-stage transfer model. (3) A third stage is designed following the initial two stages to embed information of pathological sustained phonation into the model. This stage verifies the different effects of applying sustained phonation on diagnosing the three sub-disorders, and helps boost the final diagnostic performance. RESULTS: The analysis in this study is based on clinician-labeled LMD data obtained from the Vanderbilt University Medical Center (VUMC). We find that diagnosing ETV shows sensitivity to sustained phonation within the current database. Meanwhile, the results show that the proposed multi-stage transfer learning strategy can produce (1) accuracy of 65.3% on classifying normal and other three sub-disorders all at once, (2) accuracy of 85.3% in differentiating normal, ABSD, and ETV, and (3) accuracy of 77.7% for normal, ADSD and ETV. These findings demonstrate the effectiveness of the proposed approach.

Using Momentary Assessment and Machine Learning to Identify Barriers to Self-management in Type 1 Diabetes: Observational Study.

BACKGROUND: For adolescents living with type 1 diabetes (T1D), completion of multiple daily self-management tasks, such as monitoring blood glucose and administering insulin, can be challenging because of psychosocial and contextual barriers. These barriers are hard to assess accurately and specifically by using traditional retrospective recall. Ecological momentary assessment (EMA) uses mobile technologies to assess the contexts, subjective experiences, and psychosocial processes that surround self-management decision-making in daily life. However, the rich data generated via EMA have not been frequently examined in T1D or integrated with machine learning analytic approaches. OBJECTIVE: The goal of this study is to develop a machine learning algorithm to predict the risk of missed self-management in young adults with T1D. To achieve this goal, we train and compare a number of machine learning models through a learned filtering architecture to explore the extent to which EMA data were associated with the completion of two self-management behaviors: mealtime self-monitoring of blood glucose (SMBG) and insulin administration. METHODS: We analyzed data from a randomized controlled pilot study using machine learning-based filtering architecture to investigate whether novel information related to contextual, psychosocial, and time-related factors (ie, time of day) relate to self-management. We combined EMA-collected contextual and insulin variables via the MyDay mobile app with Bluetooth blood glucose data to construct machine learning classifiers that predicted the 2 self-management behaviors of interest. RESULTS: With 1231 day-level SMBG frequency counts for 45 participants, demographic variables and time-related variables were able to predict whether daily SMBG was below the clinical threshold of 4 times a day. Using the 1869 data points derived from app-based EMA data of 31 participants, our learned filtering architecture method was able to infer nonadherence events with high accuracy and precision. Although the recall score is low, there is high confidence that the nonadherence events identified by the model are truly nonadherent. CONCLUSIONS: Combining EMA data with machine learning methods showed promise in the relationship with risk for nonadherence. The next steps include collecting larger data sets that would more effectively power a classifier that can be deployed to infer individual behavior. Improvements in individual self-management insights, behavioral risk predictions, enhanced clinical decision-making, and just-in-time patient support in diabetes could result from this type of approach.

Decoding phonation with artificial intelligence (DeP AI): Proof of concept.

OBJECTIVE: Acoustic analysis of voice has the potential to expedite detection and diagnosis of voice disorders. Applying an image-based, neural-network approach to analyzing the acoustic signal may be an effective means for detecting and differentially diagnosing voice disorders. The purpose of this study is to provide a proof-of-concept that embedded data within human phonation can be accurately and efficiently decoded with deep learning neural network analysis to differentiate between normal and disordered voices. METHODS: Acoustic recordings from 10 vocally-healthy speakers, as well as 70 patients with one of seven voice disorders (n = 10 per diagnosis), were acquired from a clinical database. Acoustic signals were converted into spectrograms and used to train a convolutional neural network developed with the Keras library. The network architecture was trained separately for each of the seven diagnostic categories. Binary classification tasks (ie, to classify normal vs. disordered) were performed for each of the seven diagnostic categories. All models were validated using the 10-fold cross-validation technique. RESULTS: Binary classification averaged accuracies ranged from 58% to 90%. Models were most accurate in their classification of adductor spasmodic dysphonia, unilateral vocal fold paralysis, vocal fold polyp, polypoid corditis, and recurrent respiratory papillomatosis. Despite a small sample size, these findings are consistent with previously published data utilizing deep neural networks for classification of voice disorders. CONCLUSION: Promising preliminary results support further study of deep neural networks for clinical detection and diagnosis of human voice disorders. Current models should be optimized with a larger sample size. LEVELS OF EVIDENCE: Level III.

FHIRChain: Applying Blockchain to Securely and Scalably Share Clinical Data.

Secure and scalable data sharing is essential for collaborative clinical decision making. Conventional clinical data efforts are often siloed, however, which creates barriers to efficient information exchange and impedes effective treatment decision made for patients. This paper provides four contributions to the study of applying blockchain technology to clinical data sharing in the context of technical requirements defined in the "Shared Nationwide Interoperability Roadmap" from the Office of the National Coordinator for Health Information Technology (ONC). First, we analyze the ONC requirements and their implications for blockchain-based systems. Second, we present FHIRChain, which is a blockchain-based architecture designed to meet ONC requirements by encapsulating the HL7 Fast Healthcare Interoperability Resources (FHIR) standard for shared clinical data. Third, we demonstrate a FHIRChain-based decentralized app using digital health identities to authenticate participants in a case study of collaborative decision making for remote cancer care. Fourth, we highlight key lessons learned from our case study.

Using Wayfinding Data to Understand Patient Travel Within a Medical Center.

Navigating through parking lots, public areas, and hallways is a stressful task for patients visiting large medical centers. Little is known about the patient experience from when they arrive at a medical center to when they check-in at their clinic. In a pilot study, we used requests for wayfinding directions from a mobile application to form a network of patient movement through the Vanderbilt University Medical Center (VUMC). From September 2016 to September 2017, patients using the wayfinding application made 3493 requests using the VUMC WalkWays application. Results show that patients frequently request directions from parking garages, on-site eateries, and the emergency room. We calculated the approximate distance patients walked to determine the extent to which associated clinical areas were co-located. Applied more generally, medical centers could use similar technologies to inform clinic placement, signage design, and resource allocation to improve the patient experience and operational efficiency.

A Progesterona na inibição do parto prematuro / Progesterone in the inhibition of preterm delivery

A progesterona tem um papel importante e bem estudado na fase inicial da gravidez. Por outro lado, sua função nos mecanismos do parto normal a termo ainda não está bem definida. Nos primatas, em oposição ao que sucede em outros mamíferos, não ocorre queda nos níveis plasmáticos da progesterona no início do trabalho de parto; para explicar isto, foram propostos mecanismos parácrinos de ativação do útero gravídico, de difícil estudo por motivos éticos. A definição do papel da progesterona no parto prematuro torna-se mais difícil pois este tipo de parto representa uma síndrome de causas variáveis. Estudos com o uso de progesterona para impedir a recorrência de parto prematuro mostram resultados animadores. Ainda há muita preocupação com esse uso por falta de estudos que determinem a dose ideal, a duração do tratamento e o acompanhamento a longo prazo dos neonatos de mães que receberam esta terapia

Células do cordão umbilical: um potencial terapêutico / Cells of the umbilical string: a potential therapeutic

O transplante de células-tronco foi inicialmente usado no tratamento de deficiências imunológicas, neoplasias hematológicas e aplasias medulares e, hoje, em outras condições, tais como talassemia ou anemia falciforme. O principal fator limitante para a quantidade transplantes alogênicos realizados é a disponibilidade de doadores. As células do sangue do cordão umbilical (CSCUs) são hoje reconhecidas como uma fonte de células-tronco hematológicas, principalmente para crianças, mas também para adultos. As vantagens das CSCUs comparadas com outras fontes de células-tronco incluem disponibilidade maior, ausência de risco para o doador e redução da rejeição (graft versus host disease). Todos estes fatos levaram ao desenvolvimento de bancos que criopreservam as CSCUs, principalmente na Europa e nos EUA. Problemas éticos e legais envolvem as CSCUs, como o direito de propriedade e da privacidade. É necessário que se criem políticas públicas para tornar as CSCUs mais facilmente desponíveis para transplante

Acetato de tamoxifeno como indutor de ovulação / Tamoxifen and ovulation induction

Em 1960 drogas não-esteróides com efeito antiestrogênico foram identificadas como possíveis contraceptivos orais. No entanto verificou-se que o seu uso induzia a ovulação em mulheres anovulatórias, e as pesquisas então se direcionaram para este campo. O acetato de clomifeno já é amplamente usado com este propósito. O acetato de tamoxifeno, pelo efeito importante antiestrogênico no tecido mamário, é considerado como droga de primeira linha no tratamento hormonal de câncer de mama assim como na prevenção primária das mulheres pós-menopausadas com alto valor de risco relativo para este câncer. Além da semelhança estrutural entre o tamoxifeno e clomifeno, vários estudos demonstraram taxas de ovulação semelhante com o uso de ambas as drogas. Neste trabalho revisamos a literatura para identificar as situações de indicação do uso do acetato de tamoxifeno após o insucesso do clomifeno ou como droga de primeira opção na indução de ovulação