Faculty Bibliography

Black and Hispanic adolescents in New York City experience high rates of pregnancy and sexually transmitted infections. A comprehensive understanding of the complexity of adolescent sexual health and family planning decisions and experiences can provide insights into the sustained disparities and inform practice, policy, and future research. The goal of this study was to explore and analyze this complexity, centering Black and Hispanic adolescent mothers as the experts on sexual and reproductive experiences from pre-pregnancy through parenting. As part of formative research for a human-centered design study, we interviewed 16 Black and Hispanic adolescent mothers living in New York City. Using situational analysis, we mapped relationships, discourse, and social structures to explore the various factors that inform adolescent sexual health decisions, in particular choices about contraception. Situational analysis found that, besides interpersonal factors, organizations and non-human elements like social media and physical birth control devices affected adolescent family planning in three social arenas: home, healthcare, and school. Within and across these arenas, adolescents lacked consistent sexual health education and contraceptive counseling and faced gendered expectations of their behaviors. Participants described parents and healthcare providers as most responsible for providing sexual health counseling yet described parents as uncomfortable or overreactive and healthcare workers as paternalistic and biased. A consideration of the many factors that inform sexual health decision-making and recognition of adolescents"™ desire for parents and healthcare providers to be a source of education can address health disparities and promote adolescent sexual health and wellbeing.

Discrimination toward trainees is a pervasive problem, with surveys showing it is often perpetrated by patients. For several years, residents and faculty in an internal medicine residency have participated in a workshop offering a framework for responding to discriminatory behavior by patients. As part of a larger effort to reinforce this teaching and promote an inclusive environment, the authors pursued a multi-pronged simulation curriculum that could be incorporated into graduate medical education programs across their institution. First, the authors conducted trainee and faculty focus groups to better understand their experiences. Qualitative data was collected, including recommendations for responding to discrimination, characteristics of the most common experiences, and trainees' own ideas for worthwhile simulation scenarios. Trainees and faculty were then brought together in a collaborative process to co-develop simulation cases that were later implemented in curricula across multiple learner levels, specialties, and contexts. Participants in these simulations reported improved comfort in responding to discrimination in the moment and/or in the wake of such incidents. Through trainee-faculty collaboration, the project yielded authentic and impactful simulation experiences for learners, while also giving trainees an opportunity to turn previous trauma into constructive learning opportunities that promote an inclusive environment.

Ensuring reliability of Large Language Models (LLMs) in clinical tasks is crucial. Our study assesses two state-of-the-art LLMs (ChatGPT and LlaMA-2) for extracting clinical information, focusing on cognitive tests like MMSE and CDR. Our data consisted of 135,307 clinical notes (Jan 12th, 2010 to May 24th, 2023) mentioning MMSE, CDR, or MoCA. After applying inclusion criteria 34,465 notes remained, of which 765 underwent ChatGPT (GPT-4) and LlaMA-2, and 22 experts reviewed the responses. ChatGPT successfully extracted MMSE and CDR instances with dates from 742 notes. We used 20 notes for fine-tuning and training the reviewers. The remaining 722 were assigned to reviewers, with 309 each assigned to two reviewers simultaneously. Inter-rater-agreement (Fleiss' Kappa), precision, recall, true/false negative rates, and accuracy were calculated. Our study follows TRIPOD reporting guidelines for model validation. For MMSE information extraction, ChatGPT (vs. LlaMA-2) achieved accuracy of 83% (vs. 66.4%), sensitivity of 89.7% (vs. 69.9%), true-negative rates of 96% (vs 60.0%), and precision of 82.7% (vs 62.2%). For CDR the results were lower overall, with accuracy of 87.1% (vs. 74.5%), sensitivity of 84.3% (vs. 39.7%), true-negative rates of 99.8% (98.4%), and precision of 48.3% (vs. 16.1%). We qualitatively evaluated the MMSE errors of ChatGPT and LlaMA-2 on double-reviewed notes. LlaMA-2 errors included 27 cases of total hallucination, 19 cases of reporting other scores instead of MMSE, 25 missed scores, and 23 cases of reporting only the wrong date. In comparison, ChatGPT's errors included only 3 cases of total hallucination, 17 cases of wrong test reported instead of MMSE, and 19 cases of reporting a wrong date. In this diagnostic/prognostic study of ChatGPT and LlaMA-2 for extracting cognitive exam dates and scores from clinical notes, ChatGPT exhibited high accuracy, with better performance compared to LlaMA-2. The use of LLMs could benefit dementia research and clinical care, by identifying eligible patients for treatments initialization or clinical trial enrollments. Rigorous evaluation of LLMs is crucial to understanding their capabilities and limitations.

BACKGROUND:Lung cancer remains a critical public health issue, presenting multifaceted challenges in prevention, diagnosis, and treatment. This article aims to review the current landscape of lung cancer research and management, delineate the persistent challenges, and outline pragmatic solutions. MATERIALS AND METHODS/METHODS:Global experts from academia, regulatory agencies such as the Food and Drug Administration (FDA) and the European Medicines Agency (EMA), the National Cancer Institute (NCI), professional societies, the pharmaceutical and biotech industries, and patient advocacy groups were gathered by the New York Lung Cancer Foundation to review the state of the art in lung cancer and to formulate calls to action. RESULTS:Improving lung cancer management and research involves promoting tobacco cessation, identifying individuals at risk who could benefit from early detection programs, and addressing treatment-related toxicities. Efforts should focus on conducting well-designed trials to determine the optimal treatment sequence. Research into innovative biomarkers and therapies is crucial for more personalized treatment. Ensuring access to appropriate care for all patients, whether enrolled in clinical trials or not, must remain a priority. CONCLUSIONS:Lung cancer is a major health burden worldwide, and its treatment has become increasingly complex over the past two decades. Improvement in lung cancer management and research requires unified messaging and global collaboration, expanded education, and greater access to screening, biomarker testing, treatment, as well as increased representativeness, participation, and diversity in clinical trials.

There is a growing literature exploring the placebo response within specific mental disorders, but no overarching quantitative synthesis of this research has analyzed evidence across mental disorders. We carried out an umbrella review of meta-analyses of randomized controlled trials (RCTs) of biological treatments (pharmacotherapy or neurostimulation) for mental disorders. We explored whether placebo effect size differs across distinct disorders, and the correlates of increased placebo effects. Based on a pre-registered protocol, we searched Medline, PsycInfo, EMBASE, and Web of Knowledge up to 23.10.2022 for systematic reviews and/or meta-analyses reporting placebo effect sizes in psychopharmacological or neurostimulation RCTs. Twenty meta-analyses, summarising 1,691 RCTs involving 261,730 patients, were included. Placebo effect size varied, and was large in alcohol use disorder (g = 0.90, 95% CI [0.70, 1.09]), depression (g = 1.10, 95% CI [1.06, 1.15]), restless legs syndrome (g = 1.41, 95% CI [1.25, 1.56]), and generalized anxiety disorder (d = 1.85, 95% CI [1.61, 2.09]). Placebo effect size was small-to-medium in obsessive-compulsive disorder (d = 0.32, 95% CI [0.22, 0.41]), primary insomnia (g = 0.35, 95% CI [0.28, 0.42]), and schizophrenia spectrum disorders (standardized mean change = 0.33, 95% CI [0.22, 0.44]). Correlates of larger placebo response in multiple mental disorders included later publication year (opposite finding for ADHD), younger age, more trial sites, larger sample size, increased baseline severity, and larger active treatment effect size. Most (18 of 20) meta-analyses were judged 'low' quality as per AMSTAR-2. Placebo effect sizes varied substantially across mental disorders. Future research should explore the sources of this variation. We identified important gaps in the literature, with no eligible systematic reviews/meta-analyses of placebo response in stress-related disorders, eating disorders, behavioural addictions, or bipolar mania.

BACKGROUND:Placebo and nocebo effects are widely reported across psychiatric conditions, yet have seldom been examined in the context of gambling disorder. Through meta-analysis, we examined placebo effects, their moderating factors, and nocebo effects, from available randomised, controlled pharmacological clinical trials in gambling disorder. METHODS:We searched, up to 19 February 2024, a broad range of databases, for double-blind randomised controlled trials (RCTs) of medications for gambling disorder. Outcomes were gambling symptom severity and quality of life (for efficacy), and drop outs due to medication side effects in the placebo arms. RESULTS:= 833) in the meta-analysis. The overall effect size for gambling severity reduction in the placebo arms was 1.18 (95%CI 0.91-1.46) and for quality of life improvement was 0.63 (0.42-0.83). Medication class, study sponsorship, trial duration, baseline severity of gambling and publication year significantly moderated effect sizes for at least some of these outcome measures. Author conflict of interest, placebo run-in, gender split, severity scale choice, age of participants or unbalanced randomisation did not moderate effect sizes. Nocebo effects leading to drop out from the trial were observed in 6% of participants in trials involving antipsychotics, while this was less for other medication types. CONCLUSION/CONCLUSIONS:Placebo effects in trials of pharmacological treatment of gambling disorder are large, and there are several moderators of this effect. Nocebo effects were measureable and may be influenced by medication class being studied. Practical implications of these new findings for the field are discussed, along with recommendations for future clinical trials.

BACKGROUND:Type 2 diabetes (T2D) contributes to significant morbidity and mortality for Chinese immigrants in the United States, exacerbated by social determinants of health (SDOH) barriers such as language barriers, limited access to healthy foods, and low health literacy. OBJECTIVE:The goal of the Integrating Cultural Aspects into Diabetes Education (INCLUDE) study is to test a social media-based intervention adapting the Diabetes Prevention Program (DPP) for Chinese immigrants alongside a culturally adapted, community-supported agriculture program. Here, we report the protocol for the INCLUDE study. METHODS:INCLUDE is a 3-year randomized controlled trial (n=150). Participants with prediabetes or at risk for T2D are enrolled and randomized into either the control or intervention group (n=75 each). Participants from the intervention group receive 2-3 culturally tailored, in-language DPP videos weekly for 12 weeks, as well as biweekly phone calls from bilingual study staff to review video content, support goal setting, and assess and address SDOH-related barriers such as food insecurity. Intervention participants will also be given produce for 10 weeks as part of the community-supported agriculture program. Weight (primary outcome), self-efficacy, diet, physical activity, and food insecurity (secondary outcomes) are measured at baseline, 3-month, and 6-month intervals. Splined linear mixed models will be used to examine group differences in longitudinal weight and other secondary outcomes. The INCLUDE study was approved by the Institutional Review Board at the NYU Grossman School of Medicine. RESULTS:Recruitment started in May 2023, with the first cohort of 75 participants enrolled and randomized into 2 groups in July 2023. The 3-month and 6-month assessment of the first-year cohort has been completed. We have recruited 75 participants for the second cohort as of July 2024. CONCLUSIONS:The INCLUDE study will serve as an innovative model for culturally adapted, multilevel interventions for underserved communities previously unable to access evidence-based diabetes prevention initiatives. Aligning with several national calls for multilevel interventions, the INCLUDE intervention will provide critical data that will inform how researchers and public health professionals address SDOH barriers faced by underserved populations and prevent diabetes. TRIAL REGISTRATION/BACKGROUND:ClinicalTrials.gov NCT05492916; https://clinicaltrials.gov/study/NCT05492916. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)/UNASSIGNED:DERR1-10.2196/65455.