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In January 2018, the Centers for Medicare and Medicaid Services (CMS) announced a waiver program for states to experiment with work requirements for “working-age, able-bodied” beneficiaries as a prerequisite for enrollment in Medicaid [1]. [...]it is not clear how each state would operationalize exemptions to the mandate [8]. [...]it is not clear how tying this to Medicaid eligibility will spur employment as has been disproved in other public assistance programs with work requirements [13].

Background Large language models (LLMs) show promise in clinical contexts but can generate false facts (often referred to as “hallucinations”). One subset of these errors arises from adversarial attacks, in which fabricated details embedded in prompts lead the model to produce or elaborate on the false information. We embedded fabricated content in clinical prompts to elicit adversarial hallucination attacks in multiple large language models. We quantified how often they elaborated on false details and tested whether a specialized mitigation prompt or altered temperature settings reduced errors. Methods We created 300 physician-validated simulated vignettes, each containing one fabricated detail (a laboratory test, a physical or radiological sign, or a medical condition). Each vignette was presented in short and long versions—differing only in word count but identical in medical content. We tested six LLMs under three conditions: default (standard settings), mitigating prompt (designed to reduce hallucinations), and temperature 0 (deterministic output with maximum response certainty), generating 5,400 outputs. If a model elaborated on the fabricated detail, the case was classified as a “hallucination”. Results Hallucination rates range from 50 % to 82 % across models and prompting methods. Prompt-based mitigation lowers the overall hallucination rate (mean across all models) from 66 % to 44 % ( p  < 0.001). For the best-performing model, GPT-4o, rates decline from 53 % to 23 % ( p  < 0.001). Temperature adjustments offer no significant improvement. Short vignettes show slightly higher odds of hallucination. Conclusions LLMs are highly susceptible to adversarial hallucination attacks, frequently generating false clinical details that pose risks when used without safeguards. While prompt engineering reduces errors, it does not eliminate them. Plain language summary Large language models (LLM), such as ChatGPT, are artificial intelligence-based computer programs that generate text based on information they are provided to train from. We test six large language models with 300 pieces of text similar to those written by doctors as clinical notes, but containing a single fake lab value, sign, or disease. We find that the LLM models repeat or elaborate on the planted error in up to 83 % of cases. Adopting strategies to prevent the impact of inappropriate instructions can half the rate but does not eliminate the risk of errors remaining. Our results highlight that caution should be taken when using LLM to interpret clinical notes. Omar et al. test six leading large language models with 300 doctor-designed clinical vignettes containing a single fake lab value, sign or disease. They show that the models repeat or elaborate on the planted error in up to 83 % of cases with a simple mitigation prompt halving the rate but not eliminating the risk.

Despite the prominence of Caenorhabditis elegans as a major developmental and genetic model system, its phylogenetic relationship to its closest relatives has not been resolved. Resolution of these relationships is necessary for studying the steps that underlie life history, genomic, and morphological evolution of this important system. By using data from five different nuclear genes from 10 Caenorhabditis species currently in culture, we find a well resolved phylogeny that reveals three striking patterns in the evolution of this animal group: (i) Hermaphroditism has evolved independently in C. elegans and its close relative Caenorhabditis briggsae; (ii) there is a large degree of intron turnover within Caenorhabditis, and intron losses are much more frequent than intron gains; and (iii) despite the lack of marked morphological diversity, more genetic disparity is present within this one genus than has occured within all vertebrates.

Efforts to control the global malaria health crisis are undermined by antimalarial resistance. Identifying mechanisms of resistance will uncover the underlying biology of the Plasmodium falciparum malaria parasites that allow evasion of our most promising therapeutics and may reveal new drug targets. We utilized fosmidomycin (FSM) as a chemical inhibitor of plastidial isoprenoid biosynthesis through the methylerythritol phosphate (MEP) pathway. We have thus identified an unusual metabolic regulation scheme in the malaria parasite through the essential glycolytic enzyme, glyceraldehyde 3-phosphate dehydrogenase (GAPDH). Two parallel genetic screens converged on independent but functionally analogous resistance alleles in GAPDH. Metabolic profiling of FSM-resistant gapdh mutant parasites indicates that neither of these mutations disrupt overall glycolytic output. While FSM-resistant GAPDH variant proteins are catalytically active, they have reduced assembly into the homotetrameric state favored by wild-type GAPDH. Disrupted oligomerization of FSM-resistant GAPDH variant proteins is accompanied by altered enzymatic cooperativity and reduced susceptibility to inhibition by free heme. Together, our data identifies a new genetic biomarker of FSM-resistance and reveals the central role of GAPDH in MEP pathway control and antimalarial sensitivity.

An education campaign for our ED physicians and physician assistants encouraged the ordering of physical therapy consults in the ED to assist with disposition of older adults. In addition to making recommendations that affect disposition, it is important that PTs in the ED be broadly trained, ideally with previous acute care experience. In future investigations we hope to quantify the decrease in length of stay for admitted patients when PT sees the patient while still in the ED.Conflict of interest disclosure The authors listed on the title page certify that they have NO affiliations with or involvement in any organization or entity with any financial interest (such as honoraria, educational grants, participation in speakers' bureaus, membership, employment, consultancies, stock ownership or other equity interest and expert testimony or patent-licensing arrangements) or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in the manuscript.

[...]approximately 1.365 million Americans with a traumatic brain injury (TBI) are treated and released from the Emergency Department (ED) annually [1]. Conflict of interest No funding, no conflicts of interest.\\n Chief complaint Head injury 874 Primary DX Concussion 300 Headache 1278a Head injury 511 Table 2 Breakdown of chief complaint and primary diagnosis for all visits during study period. # Age gender Visit 1 chief complaint Visit 1 ICD9 diagnosis Visit 2 chief complaint Visit 2 ICD9 Diagnosis 1 89 yo F HEAD injury Head injury Right hip pain Head injury, near syncope, pelvic fracture, cognitive disorder, gait disorder, normal pressure hydrocephalus, benign essential tremor, DVT unspecified laterality, fall 2 23 yo M Closed head injury with brief loss of consciousness Concussion Headache and dizziness, returning for a work note Post concussive syndrome 3 20 yo M Fell and hit head Concussion syndrome Syncope Post-concussion syndrome 4 30 yo M Head injury Head injury Headache and dizziness Post concussive syndrome 5 39 yo M Cuts on face Head injury, concussion syndrome Abdominal pain/emesis Post-concussion syndrome 6 28 yo M Head injury Scalp injury, initial encounter Head injury Concussion syndrome 7 28 yo F Headache Concussion syndrome Headache Concussion syndrome 8 86 yo M Fall Head injury Back pain Hematoma, spinous process fracture 9 28 yo F Dizziness Post concussive syndrome Dizziness Concussion with no loss of consciousness, sequela, light headedness 10 19 yo F Head injury Dizziness and giddiness Headache Concussion syndrome 11 40 yo F Headache, neck pain Neck pain Nausea Concussion syndrome, cervical abnormality 12 21 yo M Head injury Post-concussion syndrome Headache Concussion, without loss of consciousness 13 86 yo M Head injury Head injury Headache, dizziness, emesis Headache, peripheral vertigo, unspecified laterality 14 52 yo M Head injury, post concussive syndrome Head injury, post concussive syndrome Neck pain Muscle spasms of the neck 15 58 yo F Head injury Contusion of face, scalp, and neck except eye(s) Headache, nausea Headache 16 29 yo F Motor vehicle crash, headache, dizziness Closed head injury, initial encounter Hip pain Motor vehicle accident, secondary encounter, Muscle pain 17 32 yo M Assault victim, facial laceration Contusion of face, scalp, and neck except eye(s) Chest pain Chest pain 18 33 yo M Assault victim Assault Loss of conscious-ness Concussion with brief loss of consciousness 19 54 yo M Head injury, headache Concussion syndrome Fatigue Post concussive syndrome Table 3 Demographics, chief complaints, physician diagnosis and ICD 9 codes.

Earlier electrocardiogram (ECG) acquisition for ST‐elevation myocardial infarction (STEMI) is associated with earlier percutaneous coronary intervention (PCI) and better patient outcomes. However, the exact relationship between timely ECG and timely PCI is unclear. We quantified the influence of door‐to‐ECG (D2E) time on ECG‐to‐PCI balloon (E2B) intervention in this three‐year retrospective cohort study, including patients from 10 geographically diverse emergency departments (EDs) co‐located with a PCI center. The study included 576 STEMI patients excluding those with a screening ECG before ED arrival or non‐diagnostic initial ED ECG. We used a linear mixed‐effects model to evaluate D2E's influence on E2B with piecewise linear terms for D2E times associated with time intervals designated as ED intake (0–10 min), triage (11–30 min), and main ED (>30 min). We adjusted for demographic and visit characteristics, past medical history, and included ED location as a random effect. The median E2B interval was longer (76 vs 68 min, p< 0.001) in patients with D2E >10 min than in those with timely D2E. The proportion of patients identified at the intake, triage, and main ED intervals was 65.8%, 24.9%, and 9.7%, respectively. The D2E and E2B association was statistically significant in the triage phase, where a 1‐minute change in D2E was associated with a 1.24‐minute change in E2B (95% confidence interval [CI]: 0.44–2.05, p= 0.003). Reducing D2E is associated with a shorter E2B. Targeting D2E reduction in patients currently diagnosed during triage (11–30 min) may be the greatest opportunity to improve D2B and could enable 24.9% more ED STEMI patients to achieve timely D2E.

Introduction: Despite literature on a variety of social risks and needs screening interventions in emergency department (ED) settings, there is no universally accepted or evidence-based process for conducting such interventions. Many factors hamper or promote implementation of social risks and needs screening in the ED, but the relative impact of these factors and how best to mitigate/leverage them is unknown. Methods: Drawing on an extensive literature review, expert assessment, and feedback from participants in the 2021 Society for Academic Emergency Medicine Consensus Conference through moderated discussions and follow-up surveys, we identified research gaps and rated research priorities for implementing screening for social risks and needs in the ED. We identified three main knowledge gaps: 1) screening implementation mechanics; 2) outreach and engagement with communities; and 3) addressing barriers and leveraging facilitators to screening. Within these gaps, we identified 12 high-priority research questions as well as research methods for future studies. Results: Consensus Conference participants broadly agreed that social risks and needs screening is generally acceptable to patients and clinicians and feasible in an ED setting. Our literature review and conference discussion identified several research gaps in the specific mechanics of screening implementation, including screening and referral team composition, workflow, and use of technology. Discussions also highlighted a need for more collaboration with stakeholders in screening design and implementation. Additionally, discussions identified the need for studies using adaptive designs or hybrid effectiveness-implementation models to test multiple strategies for implementation and sustainability. Conclusion: Through a robust consensus process we developed an actionable research agenda for implementing social risks and needs screening in EDs. Future work in this area should use implementation science frameworks and research best practices to further develop and refine ED screening for social risks and needs and to address barriers as well as leverage facilitators to such screening.

To characterize patients with coronavirus disease 2019 (covid-19) in a large New York City medical center and describe their clinical course across the emergency department, hospital wards, and intensive care units. Retrospective manual medical record review. NewYork-Presbyterian/Columbia University Irving Medical Center, a quaternary care academic medical center in New York City. The first 1000 consecutive patients with a positive result on the reverse transcriptase polymerase chain reaction assay for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) who presented to the emergency department or were admitted to hospital between 1 March and 5 April 2020. Patient data were manually abstracted from electronic medical records. Characterization of patients, including demographics, presenting symptoms, comorbidities on presentation, hospital course, time to intubation, complications, mortality, and disposition. Of the first 1000 patients, 150 presented to the emergency department, 614 were admitted to hospital (not intensive care units), and 236 were admitted or transferred to intensive care units. The most common presenting symptoms were cough (732/1000), fever (728/1000), and dyspnea (631/1000). Patients in hospital, particularly those treated in intensive care units, often had baseline comorbidities including hypertension, diabetes, and obesity. Patients admitted to intensive care units were older, predominantly male (158/236, 66.9%), and had long lengths of stay (median 23 days, interquartile range 12-32 days); 78.0% (184/236) developed acute kidney injury and 35.2% (83/236) needed dialysis. Only 4.4% (6/136) of patients who required mechanical ventilation were first intubated more than 14 days after symptom onset. Time to intubation from symptom onset had a bimodal distribution, with modes at three to four days, and at nine days. As of 30 April, 90 patients remained in hospital and 211 had died in hospital. Patients admitted to hospital with covid-19 at this medical center faced major morbidity and mortality, with high rates of acute kidney injury and inpatient dialysis, prolonged intubations, and a bimodal distribution of time to intubation from symptom onset.