Political scientists regularly rely on a selection-on-observables assumption to identify causal effects of interest. Once a causal effect has been identified in this way, a wide variety of estimators can, in principle, be used to consistently estimate the effect of interest. While these estimators are all justified by appeals to the same causal identification assumptions, they often differ greatly in how they make use of the data at hand. For instance, methods based on regression rely on an explicit model of the outcome variable but do not explicitly model the treatment assignment process, whereas methods based on propensity scores explicitly model the treatment assignment process but do not explicitly model the outcome variable. Understanding the tradeoffs between estimation methods is complicated by these seemingly fundamental differences. In this paper we seek to rectify this problem. We do so by clarifying how most estimators of causal effects that are justified by an appeal to a selection-on-observables assumption are all special cases of a general weighting estimator. We then explain how this commonality provides for diagnostics that allow for meaningful comparisons across estimation methods—even when the methods are seemingly very different. We illustrate these ideas with two applied examples.

Recent changes to US research funding are having far-reaching consequences that imperil the integrity of science and the provision of care to vulnerable populations. Resisting these changes, the BJPsych Portfolio reaffirms its commitment to publishing mental science and advancing psychiatric knowledge that improves the mental health of one and all.

Objectives/Goals: This study tests how fiber microstructural integrity and myelination levels within the cingulum connectome are associated with information processing speed (IPS) in relapsing-remitting multiple sclerosis (RRMS). We investigate the functional impact of structural coherence, myelin content, and white matter hyperintensities (WMH) load on IPS. Methods/Study Population: Data from 63 RRMS and 25 healthy controls (HC) were used. We hypothesize that the structural integrity of the cingulum bundle and its structural network – or connectome – is distinctly associated with IPS function in people with RRMS (vs. HC) due to myelin-related plasticity across the wiring. Using diffusion spectrum imaging and high-resolution tract segmentation, we constructed individualized white matter connectomes. Diffusion quantitative anisotropy (QA) and myelin fractions (MWF) were used to quantify structural coherence and myelination. WMH load was measured with T2-FLAIR imaging. Bayesian–Pearson correlations, mixed-linear, and moderation models explored how fiber-specific QA, MWF, and WMH load relate to IPS function in RRMS, as measured by Symbol Digit Modalities Test (SDMT). Results/Anticipated Results: We theorize that (1) QA in the cingulum connectome correlates with SDMT performance dimensionally, indicating that structural coherence in the white matter supports IPS function among both groups; (2) increased myelination will strengthen the positive association between QA and SDMT scores, suggesting that connectome-specific myelin content facilitates IPS; (3) conversely, WMH load within the cingulum connectome is expected to inversely correlate with SDMT scores, reflecting the detrimental impact of lesion burden on IPS function; (4) myelination in specialized tracts within the cingulum connectome play a compensatory role to support IPS function in the RRMS group. These investigations can offer a mechanistic clue to potential neuroplastic targets for cognitive interventions in MS. Discussion/Significance of Impact: By linking white matter integrity to cognitive function at the connectome level, this study can support neuroregenerative strategies to mitigate cognitive burden in RRMS. Our findings may advance understanding of how structural coherence, tract myelination, and WMH affect IPS, shaping personalized prognostic and therapeutic interventions.

Objectives/Goals: Manual skin assessment in chronic graft-versus-host disease (cGVHD) can be time consuming and inconsistent (>20% affected area) even for experts. Building on previous work we explore methods to use unmarked photos to train artificial intelligence (AI) models, aiming to improve performance by expanding and diversifying the training data without additional burden on experts. Methods/Study Population: Common to many medical imaging projects, we have a small number of expert-marked patient photos (N = 36, n = 360), and many unmarked photos (N = 337, n = 25,842). Dark skin (Fitzpatrick type 4+) is underrepresented in both sets; 11% of patients in the marked set and 9% in the unmarked set. In addition, a set of 20 expert-marked photos from 20 patients were withheld from training to assess model performance, with 20% dark skin type. Our gold standard markings were manual contours around affected skin by a trained expert. Three AI training methods were tested. Our established baseline uses only the small number of marked photos (supervised method). The semi-supervised method uses a mix of marked and unmarked photos with human feedback. The self-supervised method uses only unmarked photos without any human feedback. Results/Anticipated Results: We evaluated performance by comparing predicted skin areas with expert markings. The error was given by the absolute difference between the percentage areas marked by the AI model and expert, where lower is better. Across all test patients, the median error was 19% (interquartile range 6 – 34) for the supervised method and 10% (5 – 23) for the semi-supervised method, which incorporated unmarked photos from 83 patients. On dark skin types, the median error was 36% (18 – 62) for supervised and 28% (14 – 52) for semi-supervised, compared to a median error on light skin of 18% (5 – 26) for supervised and 7% (4 – 17) for semi-supervised. Self-supervised, using all 337 unmarked patients, is expected to further improve performance and consistency due to increased data diversity. Full results will be presented at the meeting. Discussion/Significance of Impact: By automating skin assessment for cGVHD, AI could improve accuracy and consistency compared to manual methods. If translated to clinical use, this would ease clinical burden and scale to large patient cohorts. Future work will focus on ensuring equitable performance across all skin types, providing fair and accurate assessments for every patient.

Objectives/Goals: To screen community members for prediabetes and diabetes in the grocery stores located in urban areas, identify gaps in healthcare access, promote healthy food, teach participants about diabetes prevention and management, and learn from them via interactive community-based educational sessions. Methods/Study Population: 303 Tops Friendly Market customers in urban Buffalo, NY participated in this program. Customers without a diabetes diagnosis took a CDC Prediabetes Risk Test (score >5  =  prediabetes risk). Those with a previous diabetes diagnosis took a survey about their diabetes knowledge/management, healthcare access, and social determinants of health. Participants received a $5 voucher for fruit and vegetables. We conducted 5 educational sessions using an adult learning, participatory education approach. A $10 gift card was given for attendance. Participants shared questions/concerns and strategies to overcome barriers. We answered questions and collected information on barriers to diabetes care. Results/Anticipated Results: Seven-six participants (25%) had a diabetes diagnosis. Of these, 91% saw a doctor every 3 months, but 28% did not know the importance of HbA1c. 18% had trouble paying for medications, 15% had inadequate transportation. 227 took the Prediabetes Risk Test: 58% had a score >5, 47% had diabetes family history, 51% had hypertension, and 75% had a BMI that put them at risk for diabetes. 86% of those with a score5. 55 people (34 unique) participated in 5 sessions. We actively listened to diabetes perceptions, concerns, successes and barriers/facilitators to self-management, and discussed diabetes management strategies for heathier eating and lifestyle. Discussion/Significance of Impact: It is feasible to screen for health conditions in the supermarket setting, which can be an equalizer in enhancing access to healthcare. This study helped identify gaps in care and provided education. Importantly, people receiving this intervention lived in the poorest neighborhoods in Buffalo.

Current standard microbiological techniques are generally very time consuming, usually requiring 24–72 h to establish a diagnosis. Consequentially, contemporary clinical practices implement broad-spectrum antibiotic administration prior to pathogen detection, prompting the emergence of extremely dangerous antibiotic-resistant bacteria. Additionally, lengthy test-to-result turnover times can greatly exacerbate the rate of disease spread. Rapid point-of-care (POC) diagnostics has quickly gained importance since the SARS-CoV-2 pandemic; accordingly, we have developed a rapid four-channel POC plasmonic quantitative polymerase chain reaction (qPCR) machine (Kimera P-IV) to respond to the deficiencies in infection control. Utilizing gold nanorods (GNRs) as nano-heaters and integrating vertical cavity surface emitting lasers (VCSEL) to replace traditional Peltier blocks, the Kimera P-IV has also incorporated quantitative real-time fluorescent monitoring. Using Chlamydia trachomatis genetic material to evaluate the rapid thermocycling performance of the platform, we have generated positive amplicons in less than 13 min; however, to achieve these results, several biological reagent considerations needed to be taken into account, specifically primer design. The device can achieve a limit of detection (LoD) of <101 DNA copies, a PCR efficiency of 88.3%, and can differentiate positive from negative results with 100% accuracy. Moreover, it can also analyze C. trachomatis DNA spiked urine samples via a simple dilution, suggesting that a separate nucleic acid step may not be needed for diagnosing infections. In conclusion, the operation of the Kimera P-IV prototype places it in a unique position of POC devices to revolutionize infectious disease diagnosis.

Vaccines have revolutionised the field of medicine, eradicating and controlling many diseases. Recent pandemic vaccine successes have highlighted the accelerated pace of vaccine development and deployment. Leveraging this momentum, attention has shifted to cancer vaccines and personalised cancer vaccines, aimed at targeting individual tumour-specific abnormalities. The UK, now regarded for its vaccine capabilities, is an ideal nation for pioneering cancer vaccine trials. This article convened experts to share insights and approaches to navigate the challenges of cancer vaccine development with personalised or precision cancer vaccines, as well as fixed vaccines. Emphasising partnership and proactive strategies, this article outlines the ambition to harness national and local system capabilities in the UK; to work in collaboration with potential pharmaceutic partners; and to seize the opportunity to deliver the pace for rapid advances in cancer vaccine technology.

We describe a case of novel use of trametinib in treating arrythmia without concomitant cardiomyopathy. Our patient is a two-year-old female born with Costello syndrome due to heterozygous mutations in the HRAS gene c34 G > T p (G12C). Shortly after birth, she was diagnosed with multifocal atrial tachyarrhythmia. Her imaging studies have shown no hypertrophy or CHD. There was poor arrhythmia control despite triple antiarrhythmic therapy. Trametinib, a MEK1 and MEK2 inhibitor, was used in treating her isolated atrial arrythmia, allowing her to wean off other antiarrhythmics. Other case reports have shown trametinib to benefit certain RASopathy patients with lymphatic abnormalities, hypertrophic cardiomyopathy, and concurrent arrhythmias. This case demonstrates effective treatment of isolated arrhythmia without cardiomyopathy, broadening the potential indications for use of trametinib in certain RASopathy patients.

Evidence-based insertion and maintenance bundles are effective in reducing the incidence of central line-associated bloodstream infections (CLABSI) in intensive care unit (ICU) settings. We studied the adoption and compliance of CLABSI prevention bundle programs and CLABSI rates in ICUs in a large network of acute care hospitals across Canada.

Diffuse optical spectroscopy (DOS) techniques characterize scattering media by examining their optical response to laser illumination. Time-domain DOS methods involve illuminating the medium with a laser pulse and using a fast photodetector to measure the time-dependent intensity of light that exits the medium after multiple scattering events. While DOS research traditionally focused on characterizing biological tissues, we demonstrate that time-domain diffuse optical measurements can also be used to characterize snow. We introduce a model that predicts the time-dependent reflectance of a dry snowpack as a function of its density, grain size, and black carbon content. We develop an algorithm that retrieves these properties from measurements at two wavelengths. To validate our approach, we assembled a two-wavelength lidar system to measure the time-dependent reflectance of snow samples with varying properties. Rather than measuring direct surface returns, our system captures photons that enter and exit the snow at different points, separated by a small distance (4–10 cm). We observe clear, linear correlations between our retrievals of density and black carbon concentration, and ground truth. For black carbon concentration the correlation is nearly one-to-one. We also find that our method is capable of distinguishing between small and large grain sizes.

Clinical outcomes of repetitive transcranial magnetic stimulation (rTMS) for treatment of treatment-resistant depression (TRD) vary widely and there is no mood rating scale that is standard for assessing rTMS outcome. It remains unclear whether TMS is as efficacious in older adults with late-life depression (LLD) compared to younger adults with major depressive disorder (MDD). This study examined the effect of age on outcomes of rTMS treatment of adults with TRD. Self-report and observer mood ratings were measured weekly in 687 subjects ages 16–100 years undergoing rTMS treatment using the Inventory of Depressive Symptomatology 30-item Self-Report (IDS-SR), Patient Health Questionnaire 9-item (PHQ), Profile of Mood States 30-item, and Hamilton Depression Rating Scale 17-item (HDRS). All rating scales detected significant improvement with treatment; response and remission rates varied by scale but not by age (response/remission ≥ 60: 38%–57%/25%–33%; <60: 32%–49%/18%–25%). Proportional hazards models showed early improvement predicted later improvement across ages, though early improvements in PHQ and HDRS were more predictive of remission in those < 60 years (relative to those ≥ 60) and greater baseline IDS burden was more predictive of non-remission in those ≥ 60 years (relative to those < 60). These results indicate there is no significant effect of age on treatment outcomes in rTMS for TRD, though rating instruments may differ in assessment of symptom burden between younger and older adults during treatment.

We argue that editorial independence, through robust practice of publication ethics and research integrity, promotes good science and prevents bad science. We elucidate the concept of research integrity, and then discuss the dimensions of editorial independence. Best practice guidelines exist, but compliance with these guidelines varies. Therefore, we make recommendations for protecting and strengthening editorial independence.