Citations

Context: Early palliative care can improve end-of-life outcomes, but referrals to palliative care specialists can be delayed in the primary care setting. Objective: This pilot study assessed the effect of a machine-learning algorithm on time to palliative care in a primary care population. Methods: Patients (aged ≥18 years) were eligible if they were empaneled with a primary care provider (PCP) from July 20, 2020, through May 30, 2021. The algorithm evaluated their health records and presented patients who were predicted to have the greatest need for palliative care. Records were then reviewed by palliative care specialists, and patients were randomized in a stepped-wedge fashion to have a referral notification sent to their PCPs if unmet palliative care needs were verified. Time-to-event outcomes were evaluated with Poisson regression models. Results: Of the 127,080 patients evaluated, 934 had their health records presented for review. Some patients were repeatedly presented by the algorithm (total presentations: 1592). In the intervention arm, PCPs were prompted to order a palliative care consultation for 142 patients. The time to 0.1% of the population receiving a palliative care consultation was 60.9 days for the intervention arm versus 71.8 days for the control arm (probability of a shorter time with the intervention, 0.88). Conclusion: A machine-learning algorithm to identify palliative care needs was successfully integrated into a primary care practice. More work is needed to improve the workflow.

Background: Approximately 50,000 neonates weighing under 1,500 grams are born annually in the United States, with 15–20% delivered in smaller community hospitals without a neonatal intensive care unit. These “outborn” infants face higher risks of adverse outcomes due to limited resources. Implementing tele-resuscitation services with targeted training workshops may enhance providers’ technical skills, confidence, and perceptions of feasibility and acceptability. Methods: In January 2023, Oklahoma Children’s Hospital launched a tele-resuscitation program offering real-time, audio-video support to community hospital staff. This service targeted neonates ≤32 weeks’ gestation, <1,500 g, and those >32 weeks requiring advanced resuscitation. Workshops were hosted to give community hospital providers a first hand experience with the technology, and it included background on tele-resuscitation, technical skills training, and neonatal resuscitation simulations incorporating telemedicine. Pre- and postworkshop surveys with Likert scale questions assessed attendees’ confidence in advanced resuscitation and feasibility of integrating tele-resuscitation into practice. Results: Briefly, 20 community providers attended two interprofessional workshops, including 5 physicians (25%), 11 nurses (55%), and 4 respiratory therapists (20%). One-quarter of attendees reported prior experience with telemedicine, and one attendee reported prior experience with tele-resuscitation. Following the workshops, participant confidence in performing tasks such as intubation, umbilical line placement, needle thoracentesis, and debriefing increased significantly ( p < 0.001 for all procedures). While ratings for the appeal and applicability of telemedicine to their practice showed no significant changes ( p = 0.267 and p = 0.056, respectively), the perceived feasibility of tele-resuscitation significantly improved after the workshop ( p = 0.029). Conclusions: Neonatal tele-resuscitation workshops improved providers’ perceptions of this service and increased their acceptance of such a program. Additionally, the skill sessions and simulated resuscitations increased their confidence with procedures commonly performed during advanced neonatal resuscitation.

Abstract The Alzheimer's Disease Neuroimaging Initiative (ADNI) recently celebrated its 20th anniversary, reflecting two decades of major contributions to Alzheimer's research through open data sharing and longitudinal multimodal assessments. This review synthesizes 122 high‐impact studies using ADNI data or biospecimens from 2023 to mid‐2025 to clarify mechanisms of Alzheimer's disease (AD) progression. Studies describe impairment of glymphatic clearance and the impact of cerebral small vessel disease, trajectories of amyloid beta and tau deposition, inflammation, metabolic disturbances, synaptic dysfunction, and neurodegeneration, leading to cognitive impairment and neuropsychiatric symptoms. Multifactorial contributions from genetic and epigenetic influences, co‐pathologies and comorbidities, and mechanisms of resilience modulate disease progression. Finally, heterogeneity of clinical presentation and disease course is described in the context of multiple contributing factors, highlighting the complexity of AD. By integrating imaging, fluid biomarkers, genetics, and clinical measures, ADNI provides a comprehensive research dataset for unraveling mechanisms underlying AD progression.

Importance Currently, there are no standardized outcome domains or measures in clinical trials for facial aging. Heterogeneity in outcome domains and measurement instruments across clinical trials creates difficulty in directly comparing interventions, determining superior therapies, and developing high-quality meta-analyses. Objective To develop a core outcome set (COS) of essential domains to be reported in clinical trials evaluating the efficacy of interventions for facial aging. Evidence Review PubMed/Medline, Embase, Cochrane Central Register of Controlled Trials, and CINAHL were searched from September 2005 to September 2015. An updated search of the same databases was performed from September 2015 to February 2026. Studies were included if (1) they were randomized clinical trial or controlled clinical trial in design, (2) they assessed the efficacy or safety of an intervention for facial aging, (3) they were published in English, and (4) they involved human participants. Complementary sources, including patient interviews, were used to capture further relevant outcomes. Two rounds of Delphi surveys, followed by consensus meetings, were used to identify outcome domains considered most important by both patient and physician stakeholders. Findings The final COS consists of 6 outcome domains: (1) overall convenience of treatment; (2) time to return to normal work and social activity; (3) overall assessment of focused area of treatment (at the point in time when treatment is expected to provide peak benefit); (4) duration of treatment effect; (5) severity of persistent local or systemic adverse events, including pigmentary change, skin texture change, delayed healing, scarring, and serious adverse events; and (6) patient satisfaction with treatment. Conclusions and Relevance The 6 outcome domains identified through a Delphi consensus are recommended for reporting in future facial aging trials to ensure that outcomes that matter most to patients and clinicians are measured and that results are comparable across interventions.

ABSTRACT HLA‐C*01:155 is characterised by a single nucleotide substitution in codon 225 in exon 4.

ABSTRACT The novel HLA‐A*26:268 allele is characterised by a single nucleotide substitution in exon 7.

Abstract BACKGROUND Remote, smartphone‐based cognitive testing may improve access to cognitive assessments for Alzheimer's disease and related dementias. We evaluated the feasibility, reliability, and validity of unsupervised smartphone‐based cognitive tests in a registry‐based cohort. METHODS Adults without a record of cognitive impairment ( N = 1815; ages 18–92) were recruited from the University of California, San Francisco (UCSF) Brain Health Registry to complete three unsupervised smartphone cognitive testing sessions within 2 weeks. Reliability was assessed with correlations between sessions. Linear regression models tested associations of smartphone tasks with demographics, self‐ and informant‐rated cognitive concerns, and web‐based cognitive testing (Cogstate Brief Battery). RESULTS Adherence was high (82.2%) and usability favorable. Test–retest reliability was moderate to strong ( ρ ’s = 0.61–0.85, all p ’s < 0.001). Lower smartphone scores were associated with older age, lower education, cognitive concerns, and worse Cogstate performance. DISCUSSION Findings support the feasibility, reliability, and validity of remote digital assessments in adults without a record of cognitive impairment.