Lu Tian

BACKGROUND: Understanding the publication rates and citation patterns of conference abstracts is critical for program committees and investigators alike. However, the downstream scholarly outcome of conference abstracts remains poorly characterized. We hypothesized that across contemporary US trauma-related scientific meetings, a substantial proportion of conference abstracts would not progress to peer-reviewed publications, and that publication rates and citation patterns would vary by presentation format and conference. METHODS: We conducted a cross-sectional study of abstracts presented at four major US trauma surgery conferences from 2017 to 2019. We identified peer-reviewed manuscripts associated with conference abstracts via Google Scholar and PubMed. We collected presentation format, publishing journal, 2024 journal impact factor, and citation count. A cohort of directly submitted peer-reviewed manuscripts was also analyzed. Mixed-effects Poisson regression models assessed the association between presentation and citation counts, adjusting for publication timing. RESULTS: Of 1,157 abstracts, 762 (66%) were published as peer-reviewed manuscripts. Most manuscripts were published in the Journal of Trauma and Acute Care Surgery ( JTACS ) (55%), with 5 in journals with a higher-impact factor than JTACS . On regression analysis, abstracts presented as oral presentations, and published in journals with an impact factor higher than JTACS , were associated with a greater number of citations. When comparing manuscripts presented as abstracts to directly submitted peer-reviewed manuscripts, abstract presentation was associated with 22% more citations (incidence rate ratio: 1.22; 95% CI [1.16–1.29]; p JTACS , published manuscripts originating from presented abstracts demonstrated a modestly higher citation count compared with directly submitted manuscripts. Our findings highlight an opportunity to better understand how conference time and investigator effort translate into downstream scholarly output. LEVEL OF EVIDENCE: Prognostic/Epidemiological; Level IV.

Background: Postpartum sleep is a unique construct, and existing sleep patient-reported outcome measures (PROMs) do not adequately address some of its fundamental domains. We aimed to develop postpartum-specific extension items to the PROMIS Sleep Disturbance (SD) and Sleep Related Impairment (SRI) item banks. Methodology: The postpartum-specific SD and SRI items were developed to add to the existing PROMIS SD and SRI item banks. Postpartum-specific PROMIS items were generated based on findings from literature characterizing postpartum sleep, including systematic reviews and qualitative interviews with patients and experts from diverse specialties. The candidate items were subsequently iteratively adapted and selected through a modified Delphi process involving a panel of 21 stakeholders including experts in obstetrics, sleep, psychiatry, patients and partner representatives. Items achieving consensus with ≥ 70% stakeholder agreement were subsequently piloted and refined through recorded cognitive debriefing interviews with postpartum individuals until no further suggestions were generated in 5 consecutive interviews. Results: Nine new postpartum SD items, and four new SRI items were developed to extend the existing PROMIS SD (27 items) and SRI item banks (16 items). The new items covered six concepts (psychological factors, infant-related sleep disruption, pain, social support, feeding of infant, and sleep interference with self-care, maternal-neonatal bonding, and energy), and ten underlying dimensions (worry, post-traumatic stress symptoms, maternal-neonatal bonding, activities of daily living, pain, neonatal awakenings, maternal awakenings, latency, social support, infant feeding) of postpartum sleep. Conclusions: We developed postpartum-specific extensions to the existing PROMIS SD and SRI item banks using the National Institute of Health PROMIS framework. Next steps include evaluation of their psychometric properties, clinical validation, and studies to establish clinically important differences in scores for clinical application.

Was not provided for this article.

Motivation: Spatial omics studies compare cell–cell organization across samples, but most methods model between-sample variability while treating sample-level spatial estimates as error-free. Overlooking within-sample uncertainty can distort inference in heterogeneous cohorts, motivating methods that explicitly quantify and propagate this uncertainty into cohort-level analyses. Results: We present Pooled ANalysis Of VaRiance-Aware Modeling and Inference of Colocalization (PANORAMIC), a hierarchical framework for spatial colocalization analysis that uses edge-corrected neighborhood enrichment to estimate local cell-type colocalization, spatial bootstrapping to quantify within-sample uncertainty, and multilevel random-effects meta-analysis to propagate this uncertainty across samples, patients, and conditions. In simulations, PANORAMIC improved recovery of within-sample uncertainty and between-sample heterogeneity relative to naive estimators across diverse spatial settings and progressive data degradation. Applied to a colorectal cancer tissue microarray profiled by multiplexed immunofluorescence imaging, PANORAMIC identified stronger B- and T-cell colocalization in tumors with Crohn’s-like reaction than in tumors with diffuse inflammatory infiltration, together with tighter higher-order immune organization consistent with immune aggregates. These findings were missed using standard methods, showing that propagating within-sample spatial uncertainty can improve cohort-level inference in spatial omics studies. Availability and implementation: PANORAMIC is released as an open-source R package at https://github.com/plevritis-lab/panoramic and archived on Zenodo at https://doi.org/10.5281/zenodo.19927197.

Comorbidity indices are widely used in clinical research to summarize disease burden. However, traditional indices were developed decades ago in limited populations using fixed weights that do not reflect the diversity of patients in modern healthcare. We present the Personalized Comorbidity Score (PCS), a data-driven framework for context-dependent comorbidity scoring designed to capture patient complexity while remaining accessible for broad research adoption. PCS was developed using Epic Cosmos, a large national EHR network encompassing over 8 million adult inpatient encounters from 2015 to 2020, with comorbidities defined using AHRQ Clinical Classifications Software Refined categories. Models were developed separately across eight age-sex subgroups using LASSO-penalized Cox regression for feature selection and restricted mean survival time for score derivation. PCS is available in two versions: PCS Core, incorporating age, sex, and comorbidities, and PCS Extended, which additionally incorporates socioeconomic and geographic variables. PCS Core and PCS Extended achieved AUROCs of 0.812 and 0.813 for one-year mortality, outperforming traditional indices (AUROC, 0.714-0.730). PCS demonstrated consistently lower subgroup calibration error across demographic and socioeconomic groups without including race or ethnicity as model features. PCS was further evaluated in two complementary external EHR datasets (Stanford Health Care and MIMIC-IV) with distinct patient populations and data structures, where it consistently outperformed traditional indices. Open-source R and Python packages are provided to support broad adoption. PCS provides an updatable framework for comorbidity measurement that is accurate, context-dependent, and designed to evolve alongside clinical practice.

In most randomized clinical trials evaluating intervention effects on time‐to‐event outcomes, the log‐rank test combined with Cox's hazard ratio (HR) estimation has been the standard test/estimation approach for decades. While this traditional framework offers several advantages, concerns have been raised about HR's limitations as a summary measure of the magnitude of intervention effects. To address these concerns, an alternative approach based on the average hazard (AH) has been proposed. AH is defined as a functional of the survival function of the event time distribution and is interpreted as an average incidence rate over a given time window. Although its theoretical properties and nonparametric inference procedures have already been established, practical guidance for its use in the primary analysis of randomized clinical trials (RCTs) remains limited. In this paper, we address two key aspects critical to the implementation of AH in RCTs: (1) design considerations when the AH‐based analysis is used for the primary analysis, and (2) tools for identifying the appropriate timing of the analysis. We provide a framework for study design using AH and introduce a new R package, survAHtools , to support its practical application in RCTs.

1. Change from baseline (CFB) of a continuous outcome has been a popular metric to quantify clinical, physiological, and molecular change related to evolvement of disease status. In many randomized...