Faculty Bibliography

PURPOSE/OBJECTIVE:To evaluate the effects of extracorporeal ultra-high-fluence corneal cross-linking (ECO-CAIRS) on the dehydration and rehydration behavior of porcine corneal stromal ring segments in an experimental model simulating the preparation of corneal allogenic intrastromal ring segments. SETTING/METHODS:ELZA Institute, Zurich, Switzerland. METHODS:Corneal ring segments (two per cornea) were prepared from freshly enucleated porcine corneas (n=48) and randomly divided into three groups: controls (no CXL, Jerky technique), extracorporeal ultra-high-fluence CXL at 30 J/cm2, and 60 J/cm2 (n=32 each). After epithelial removal and ring preparation, segments in the control group were soaked in balanced salt solution (BSS), while those in the experimental groups were soaked in riboflavin solution (Ribo-Ker, EMAGine AG, Zug, Switzerland) for 10 minutes. Each group subsequently underwent a total dehydration period of 45 minutes, which included both UV-A irradiation and waiting phases for the CXL-treated groups, followed by a 10-minute rehydration phase using BSS. Segment thickness was measured by calibrated imaging at baseline, after dehydration, and after rehydration. RESULTS:Baseline structural thickness values did not differ significantly between groups. Both CXL-treated groups demonstrated significantly greater dehydration (p<0.01) and reduced rehydration (p<0.01) when compared to controls. No significant differences were found between the two CXL-treated groups. Ultra-high-fluence cross-linking enhanced segment dehydration and slowed rehydration. CONCLUSIONS:ECO-CAIRS effectively modulates the dehydration and rehydration of corneal ring segments, which may improve handling and implantation characteristics during surgery. These findings support the potential clinical benefit of CXL in CAIRS procedures. Further in vivo studies are required to confirm long-term behavior and biomechanical impact.

BACKGROUND/UNASSIGNED:Nontuberculous mycobacteria pulmonary disease (NTM PD) has been increasing in the United States, and New York City is an important hotspot, with a high burden of disease. RESEARCH QUESTION/UNASSIGNED:We assessed how neighborhood-level risk factors influenced NTM PD prevalence in New York City. STUDY DESIGN AND METHODS/UNASSIGNED:We used outpatient claims data from hospitals participating in the Patient-Centered Outcomes Research Institute network, compiled by the INSIGHT Clinical Research Network at Weill Cornell Medicine. NTM PD period prevalence (referred to here as 'prevalence') was estimated by New York City neighborhood for the study period 2012 through 2022. A case was defined as someone with at least one claim for NTM PD. De-identified NTM PD case data and demographic data were analyzed by neighborhood of residence at diagnosis. Using prevalence estimates, we detected high- and low-prevalence regions within New York City and associated these estimates with demographic, clinical, socioeconomic, and environmental neighborhood-level factors using Poisson regression and backward elimination of covariates. RESULTS/UNASSIGNED:Overall, 6,169 NTM PD cases were identified among persons receiving care across 17 private New York City hospitals. The mean age was 67.5 years, 68.7% were female, and 57.3% were White. Over the study period, NTM PD prevalence increased throughout New York City, and median year built of housing units, median income, and median age of residents were significant neighborhood-level risk factors. The highest prevalence neighborhoods were in Manhattan, while the lowest prevalence neighborhoods were in Brooklyn and Staten Island. INTERPRETATION/UNASSIGNED:Our findings indicate that neighborhood-level access to care may explain the heterogeneity in NTM PD prevalence among New York City neighborhoods, as higher income, newer neighborhoods exhibited the highest NTM PD prevalences. Future studies should examine the extent of undetected NTM PD in New York City, particularly in low-income areas.

BACKGROUND AND OBJECTIVES/OBJECTIVE:GLP-1-based medications have rapidly reshaped the landscape of obesity treatment. Semaglutide was approved for obesity treatment in 2021 in the US and 2022 in Europe, sparking global interest, and the GLP-1/GIP dual-agonist tirzepatide has demonstrated even greater efficacy. However, survey-based data on who uses or considers these medications-particularly across socioeconomic groups, BMI categories, and weight management experiences-remain limited. SUBJECTS/METHODS/METHODS:A nationally representative online survey of Finnish adults (n = 1729, of which 1693 were included) was conducted in June 2025 via a market research company using quota sampling. Use and awareness of GLP-1 medications for obesity were measured with a single item listing widely known brands-Ozempic, Wegovy, Zepbound, and Mounjaro-to aid recognition. In regression analyses, current, past, and potential users (n = 322) were combined. RESULTS:In total, 3.5% reported current and 2.0% past use, and 13.5% expressed interest in future use. Notably, ~40% of individuals with obesity reported no interest. Bivariate analysis showed that current use was more common among women, those aged 50-69, those with household income exceeding €70,000, individuals with higher BMI, frequent weight loss attempts, experiences of weight-based discrimination, and self-blame. Multivariable analysis showed that current, past, and potential use had strongest associations with BMI ≥30.0 kg/m², repeated or persistent weight loss attempts, experiences of discriminatory treatment due to weight, self-blaming thoughts and hopeful perceptions of GLP-1 medications. Concern about serious health risks was associated with lower likelihood of use or interest. CONCLUSIONS:GLP-1-based medications are gaining recognition, but uptake remains heterogeneous and many individuals with obesity remain uninterested. Concerns about potential health risks persist. Attitudes toward these medications are shaped not only by weight status but also by prior weight management experiences and perceptions. Clinical communication should be sensitive to these factors.

The upper extremity is a structure of incredible complexity able to perform unique tasks that span from the most delicate and intricate to highly strenuous and forceful. Fully functional and aesthetic replacement of the upper extremity after loss remains a medical and technological aspiration. Meaningful advancements continue to be made in all areas of upper extremity limb loss, specifically in traditional surgical reconstruction and prosthetics. This has generated a new field of treatment that is most accurately titled Bionic Reconstruction. Essentially, successful bionic reconstruction allows for the human body to effectively communicate and work in concert with the man-made technology.

IMPORTANCE/UNASSIGNED:Patient portal messaging volume has increased substantially and is linked to clinician burnout. In response, health systems are increasingly adopting large language models (LLMs) to generate draft replies to patient messages, yet little is known about how patients interpret and evaluate artificial intelligence (AI) involvement in this communication channel. OBJECTIVE/UNASSIGNED:To understand how patients perceive AI-drafted responses to electronic patient portal messages and to identify implications for patient-centered implementation. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:This qualitative study used vignette-based prompts mirroring scenarios from a prior survey on patient preferences regarding AI-drafted online messaging. Participants included a sample of 40 adult patients from a large academic health system who had previously been surveyed about AI-drafted portal messaging. Recruitment sought increased representation of individuals from minoritized racial and ethnic groups, older patients, and participants with lower satisfaction with AI-drafted messaging in prior survey responses. Data were collected between April and August 2025. EXPOSURE/UNASSIGNED:One-time interviews conducted via videoconference. Interviews included vignettes and draft messages varying in tone, length, and implied authorship, alongside questions about messaging preferences, AI, and health care. MAIN OUTCOMES AND MEASURES/UNASSIGNED:Patient perspectives regarding (1) portal messaging preferences, (2) comfort with AI-drafted replies, (3) preferred message tone and content, and (4) AI disclosure expectations. RESULTS/UNASSIGNED:This study of 40 patients included 30 women (75.0%), 9 men (22.5%), 14 Black or African American participants (35.0%), 13 White participants (32.5%), and 13 patients aged 65 years or older (32.5%). Patients described portal messaging as transactional, prioritizing timely problem resolution over relational depth. Patients expressed high comfort with AI-drafted messages, conditional on clinician oversight. Patient preferences for tone and expressions of empathy varied across individuals and contexts and were driven less by whether messages appeared AI-like or human-like than by whether tone, length, and detail fit the purpose and stakes of the communication. Participants broadly endorsed AI disclosure, describing transparency as important for trust, but differed in preferred disclosure timing and format. CONCLUSIONS AND RELEVANCE/UNASSIGNED:In this qualitative study of patient perspectives on AI-drafted portal messaging, patients supported AI use when it improved communication efficiency, so long as clinicians reviewed and remained accountable for messages. Implementation should prioritize clinician oversight, context-sensitive communication standards, standardized disclosure practices, and monitoring for downstream effects on quality, equity, and patient trust and safety.

Despite widespread adoption of electronic health records (EHRs), health systems remain heavily dependent on faxed documents for critical patient information. At New York University Langone Health, this represents nearly 20 million document-pages per year - laboratory results, consult notes, imaging prescriptions, refill requests, and prior authorizations - each requiring manual review and indexing. These workflows are time consuming, involve multiple staff touchpoints, can be prone to error, and may create delays for patients awaiting follow-up care. To provide the highest quality of care to patients and to augment staff experience, the authors developed and deployed an Intelligent Document Processing (IDP) solution leveraging existing enterprise technologies for document management, robotic process automation, data classification and extraction, and EHR-integrated indexing. This solution identifies electronically faxed documents, extracts patient and provider information, matches the EHR record, sorts the documents into clinical or administrative queues, and assigns a document type for indexing. To ensure patient safety, documents that cannot be confidently processed are routed to an exceptions folder for manual review. The IDP solution was deployed and monitored at one high-volume multispecialty practice from August to October 2025. In this time, the system processed approximately 20,000 document-pages, representing 13,700 faxes or scans. Of these, 8500 (62%) were successfully classified to one of the predefined in-scope clinical and administrative document types that the system was trained to recognize (e.g., laboratory results, pathology and radiology reports, procedure notes such as colonoscopy or endoscopy, medication- and insurance-related authorizations, and consult or therapy reports); based on the classification, they were then routed to the appropriate work queue for indexing. The remaining 38% required manual review - 32% were identified as being outside the target set of document types, and 6% were flagged as exceptions (e.g., multiple patients in one fax, document longer than 20 pages). The cost to operate was approximately 1.5 U.S. cents per page during the pilot, significantly less expensive than competitive industry offers of approximately 15 U.S. cents per page. Implementation required not only technical integration, but also operational redesign. Key hurdles included applying existing technologies to a single orchestrated solution, managing the unclassified documents workload, aligning document type taxonomies between systems, handling provider name variation, and training clinical staff. Change management was paramount, as individual practices had developed varied and entrenched fax workflows that required reengineering and preproduction dress rehearsals prior to go-live. This experience demonstrates the potential for an artificial intelligence (AI)-enabled IDP solution to meaningfully reduce administrative burden, improve timeliness and accuracy of document indexing, and unlock structured data from scanned pages. Never before had these practices been able to quantify and route faxed documents automatically. Although challenges remain in scaling across diverse workflows, this case illustrates how health systems can pragmatically deploy AI using existing infrastructure to improve efficiency, reduce staff burden, and support better care delivery.

BACKGROUND:Reporting guidelines for implementation strategies are important for transparency, measurement, and replicability. Yet, recent calls within implementation science highlight that foundational research commitments to advance minority health remain underspecified in current implementation strategy reporting guidelines. METHODS:In response, our research team sought to expand Proctor and colleagues' guidelines for reporting and specifying implementation strategies to advance minority health. We first identified and synthesized key elements for specification from relevant literature. We then applied potential supplemental reporting criteria to our Literacy Promotion for Latinos (LPL) study as a case example to iterate and refine the suggested supplemental criteria. LPL implemented text messages and streamlined access to community resources as implementation strategies to enhance uptake of Reach Out and Read, an evidence-based pediatric clinic-based literacy promotion intervention specifically among Latino families. RESULTS:Our suggested supplemental reporting criteria integrate elements of community-engagement and health disparities research foundational for advancing minority health. We offer a new category, (1) Prioritize It, to describe processes of defining and prioritizing the (a) health outcome gap of interest and (b) performance gap of the evidence-based intervention. We add criteria to Proctor and colleagues' (2) Specify It category, which outlines the description of the implementation strategies, to describe processes of implementation strategy (a) selection, (b) development, and (c) tailoring. Lastly, we suggest a new category, (3) Evaluate It, to include community-engaged evaluation processes. In our case example, we explain the team's processes and engagement of relevant community members and partners in (a) prioritizing disparities in linguistic and socio-emotional development for Latino children related to school readiness and performance gaps in Reach Out and Read for families in local clinic communities; (b) specifying the selection, development, and tailoring of implementation strategies to focus on social and structural drivers of this health disparity; and (c) facilitating community partnerships to evaluate the strategies. CONCLUSIONS:Expansion of implementation strategy criteria for reporting can help ensure commitments to advancing minority health are fulfilled. Future efforts should convene researchers and community partners to create expert consensus on the specifications required for implementation strategies for addressing drivers of disparities and advancing minority health. TRIAL REGISTRATION/BACKGROUND:ClinicalTrials.gov, Literacy Promotion for Latinos Study, NCT04609553, first posted October 30, 2020: https://clinicaltrials.gov/ct2/show/NCT04609553.

BACKGROUND:Robotic-assisted total joint arthroplasty (RTJA) is increasingly used to improve patient outcomes and reduce revision rates in total joint arthroplasty (TJA). With robotic-assisted total knee arthroplasty (RTKA) projected to exceed 70% of cases by 2030, concern exists about whether orthopaedic residents are being adequately trained. METHODS:Orthopaedic arthroplasty fellows between 2023 and 2025 completed an anonymous electronic survey assessing exposure to RTJA and conventional TJA (CTJA), impact on fellowship selection, and projected future use. Incomplete responses and non-fellows were excluded. RESULTS:Of 60 respondents, 78% were exposed to RTKA, 52% to robotic-assisted unicompartmental knee arthroplasty (RUKA), and 48% to robotic-assisted total hip arthroplasty (RTHA). Satisfaction with training was higher for conventional procedures: 82 versus 53% (TKA), 97 versus 32% (THA), and 23 versus 31% (UKA). Most felt comfortable performing conventional TKA (CTKA) and THA (CTHA) independently; among robotic procedures, only RTKA had similar comfort levels. Fellowship selection was influenced by a desire for balanced robotic and manual experience (66.7%). While 42% did not believe robotics should be required in residency, most agreed it improved understanding and performance (68% RTKA, 61% RUKA, and 75% RTHA respondents). Robotic exposure during residency was associated with higher satisfaction and preparedness (P < 0.001) and increased support for requiring training in RUKA (P = 0.015) and RTHA (P = 0.006). Regional differences in exposure and satisfaction were also observed. Fellows planning to use robotics in greater than 50% of future cases were more likely to choose robotics-focused fellowships (P = 0.003). CONCLUSION/CONCLUSIONS:Arthroplasty fellows reported high satisfaction with conventional arthroplasty training, whereas satisfaction and self-reported preparedness for robotic-assisted procedures were lower. Prior robotic exposure during residency was associated with higher satisfaction and preparedness, and fellows expressed interest in balanced robotic and manual fellowship experiences. These findings provided a baseline of current trainee perceptions.

CONTEXT/BACKGROUND:: Growth hormone deficiency (GHD) can lead to significant growth and developmental issues, necessitating long-term treatment. Once-weekly somatrogon is a long-acting recombinant human growth hormone analogue, approved for treatment of children with GHD. OBJECTIVE:: Assess the long-term safety and efficacy of somatrogon in children with GHD. DESIGN/METHODS:: Open-label extension (OLE) study following an initial 12-month open-label phase 2 study. SETTING/METHODS:: Conducted at 14 centers across Hungary, Bulgaria, Belarus, Ukraine, Russia, Greece, and the US. PATIENTS OR OTHER PARTICIPANTS/METHODS:: Fifty-three children with GHD (67.9% male, mean age 6.0±2.1 years) completed the phase 2 study and 48 entered the OLE. INTERVENTION(S)/METHODS:: Participants received once-weekly somatrogon for up to 9 years. MAIN OUTCOME MEASURE(S)/METHODS:: Safety endpoints included adverse events (AEs), antidrug antibodies (ADAs), local site injection reactions, and insulin-like growth factor-I SDS. Efficacy endpoints included annual height velocity (HV), change in height SDS, and annual bone maturation. RESULTS:Treatment-emergent AEs (TEAEs) incidence was 52.1% at OLE year (Y) 1, decreasing to 23.8% by Y9. Serious AEs were rare with only isolated cases reported. ADA positivity was observed, but no neutralizing antibodies were found. No correlation between ADA presence and TEAE incidence or severity was noted, nor was there a difference in growth based on ADA presence. Mean annualized HV remained > 5 cm/year, and height SDS improved from -4.0 at baseline to -0.2 by Y8. No changes in the ratio of bone age/chronological age were observed. CONCLUSIONS:Once-weekly somatrogon is a well-tolerated and a viable long-term treatment for GHD in children. More real-world data are needed. Clinicaltrials.gov: NCT01592500.

BACKGROUND/UNASSIGNED:Racial and ethnic disparities are a concern in lung cancer screening. OBJECTIVE/UNASSIGNED:To investigate the performance of risk prediction models to define screening eligibility across 4 U.S. racial and ethnic groups. DESIGN/UNASSIGNED:Cohort study. SETTING/UNASSIGNED:United States, Lung Cancer Cohort Consortium. PARTICIPANTS/UNASSIGNED:641 830 participants aged 50 to 80 years with a smoking history from 12 U.S. cohorts, including 6390 Asian, 9781 Hispanic, 39 872 non-Hispanic Black, and 585 787 non-Hispanic White participants. MEASUREMENTS/UNASSIGNED:Calibration and discrimination were quantified for 16 lung cancer prediction models. Then, screening-related metrics were calculated after applying model thresholds to select the same number of eligible participants as the 2021 criteria from the U.S. Preventive Services Task Force (USPSTF-2021). These included eligibility, sensitivity, and efficiency measured as estimated number needed to screen (NNS; the ratio between participants and lung cancer cases) for each strategy or prediction model in each racial and ethnic group. RESULTS/UNASSIGNED:General patterns across the 16 models included substantial underestimation of lung cancer risk in non-Hispanic Black participants (expected-observed ratio < 0.75 for 11 of 16 models), lower discrimination in Asian participants than all other groups (13 of 16 models), and lower discrimination in non-Hispanic Black than non-Hispanic White participants (15 of 16 models). When a same-sized screening-eligible population as USPSTF-2021 (38.0%) was enforced, all risk-based strategies achieved better average estimated screening efficiency and reduced racial and ethnic differences in efficiency compared with USPSTF-2021. The Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial Model 2012 (PLCOm2012) and Life Years gained From Screening-Computed Tomography model (LYFS-CT) performed best (mean estimated NNS, 36.5 [SD, 8.8] and 40.1 [SD, 8.2], respectively). However, no strategy could simultaneously optimize eligibility, sensitivity, and efficiency while also reducing racial and ethnic differences. LIMITATION/UNASSIGNED:Smaller sample for Asian and Hispanic participants. CONCLUSION/UNASSIGNED:To optimize efficiency and minimize its variation across racial and ethnic groups, risk-based strategies were superior to USPSTF criteria. Further optimization of prediction models for the diverse U.S. population is needed. PRIMARY FUNDING SOURCE/UNASSIGNED:U.S. National Cancer Institute, Lung Cancer Research Foundation, and Cancer Research UK.