United States

Title
Osteopathic Diagnoses and Treatments Among United States Trauma Patients

Authors:
Hanna Harris
Aviya Distefano
Kaitlin M Bowers, DO
Dhimitri A Nikolla, DO, MS

Affiliations:
Department of Emergency Medicine, Jerry M. Wallace School of Osteopathic Medicine, Campbell University, Lillington, USA.
Department of Emergency Medicine, Jerry M. Wallace School of Osteopathic Medicine, Campbell University, Lillington, USA.
Department of Emergency Medicine, Jerry M. Wallace School of Osteopathic Medicine, Campbell University, Lillington, USA.
Department of Emergency Medicine, Allegheny Health Network – Saint Vincent Hospital, Erie, PA, USA.

Background
Osteopathic manipulative medicine (OMM) encompasses a wide range of diagnostic and treatment techniques for acute and chronic conditions. However, few studies have evaluated the use of OMM on acutely injured patients, and none have done so on a nationwide scale. 

Objectives
We aimed to estimate the incidence of OMM use among trauma patients in United States (US) emergency departments (ED) and provide demographic and clinical characteristics of these osteopathic trauma encounters. 

Methods
Design and Setting
We performed a retrospective, secondary analysis of the 2018 to 2021 Nationwide Emergency Department Sample (NEDS) datasets, Healthcare Cost and Utilization Project (HCUP), Agency for Healthcare Research and Quality. Data are from encounters from hospital-owned US EDs. The Allegheny Health Network Institutional Review Board deemed the study exempt.

Patients and Interventions
We included all ED encounters with a primary or secondary injury diagnosis identified by the NEDS I10_INJURY variable and an osteopathic diagnosis or treatment. Osteopathic diagnoses were identified by the International Classification of Diseases-10 (ICD-10) Clinical Modification (CM) codes M9900-M9909. Osteopathic treatments were identified by either ICD-10 Procedure Coding System (PCS) codes 7W00X0Z-7W09X9Z or Current Procedural Terminology / Healthcare Common Procedure Coding System (CPT/HCPCS) codes 98925-98929.

Main Outcome Measures
We used the svyciprop function with the beta method from the survey package in R to calculate the weighted incidence of osteopathic trauma encounters per 100,000 trauma patients accounting for complex survey design. We provided unweighted descriptive statistics for demographic and clinical characteristics. 

Results
Among over 24 million trauma encounters, we identified 1,356 with an osteopathic diagnosis or treatment. The weighted incidence of osteopathic encounters was 6.0 (95% CI 4.0 to 8.5) per 100k visits. The median patient age was 50 years (IQR 32 to 66) and 753 (55.5%) were female. 459 (33.8%) encounters in level one or two trauma centers. 359 (26.5%) had injuries due to a fall and 536 (39.5%) were admitted. 852 (62.8%) had an ISS of ≤1 with a median score of 1 (IQR 1 to 4) and a maximum score of 50. The most common ICD-10-CM code was segmental and somatic dysfunction of thoracic region (485 of 2,877 codes, 16.9%), and 553 of 1,052 (52.6%) of ICD-10-PCS codes were for fascial release. 

Conclusion
Osteopathic encounters were uncommon among acutely injured patients in US EDs. Most had minor injuries, but over a third were admitted. Additional research is needed to describe the indications for OMM among acutely injured patients, particularly if OMM is used to treat acute injury extrinsic to these locations.

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Osteopathic Diagnoses and Treatments Amongst United States Trauma Patients

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Background
Prostate cancer ranks as the second most common cause of cancer-related mortality among males. A transperineal template-guided core biopsy procedure is essential in the diagnosis and grading of prostate cancer. Various factors influence the quality of the histological sample that is produced from the biopsy, including tissue fragmentation, needle insertion speed, needle deflection, and sheath cutting speed, all of which relate to friction force. In instances where a biopsy core fails to meet established clinical standards, an additional core extraction is necessary, resulting in heightened patient discomfort and an increased risk of infection. The interaction between the biopsy needle and the needle guide of the template has been an overlooked component of the procedure. The objective of this study is to examine alternative configurations for the needle guide in order to optimize the reduction of needle guide frictional forces.

Methods
A virtual simulation of this interaction using various configurations (e.g., Parallel 3-point, Perpendicular 5-point, etc.) of the needle guide was designed as using SIMULIA Abaqus (version 2023.HF2), and a Finite Element Analysis was performed to record the friction force between the needle and guide in these different configurations. An equation was derived according to solid mechanics to correlate with results of the simulation to ensure real-world applicability.

Results
The derived equation estimated a mean peak friction force of 8.43 ± 2.53N, which was approximated by the Control condition (6.0N), suggesting that the simulation accurately represented true material properties. The Parallel 3-point template configuration (0.13N) most dramatically reduced friction force compared to the Control (6.0N) or Perpendicular 5-point and 3-point configurations (4.0N and 1.2N, respectively). The total area of contact between the needle guide and the needle was smallest in the Parallel 3-point template configuration (0.014mm2) compared with the Control (3.99mm2) or Perpendicular 5-point and 3-point configurations (0.09mm2 and 0.05mm2, respectively).

Conclusion
Several configurations of the needle guide are superior to the standard needle guide, with the optimal configuration being the Parallel 3-point template. These findings advocate for the optimization of the conventional transperineal prostate biopsy needle guide to reduce the friction between the biopsy needle and the template, potentially reducing the number of passes required to obtain a histologically viable core specimen and mitigating the risk of patient infection and postprocedural complications.

Needle Guide Friction Reduction Utilizing Contact Surface Structure

Background
Immune checkpoint inhibitors (ICIs) have revolutionized non-small cell lung cancer (NSCLC) treatment, yet predicting durable benefit and toxicities like immune-related pneumonitis (IRP) remains challenging. We developed a novel deep-radiomics framework using baseline CT imaging to simultaneously forecast ICI efficacy and IRP risk, addressing critical unmet needs in precision immunotherapy selection. This integrates a patented CNN-enhanced methodology for unified tumor segmentation and multi-level feature extraction in a single neural network pass.
Methods
We analyzed baseline CT scans from 102 advanced NSCLC patients (stage IIIB-IV) receiving ICIs. Models predicted durable clinical benefit (DCB; response/stable disease ≥24 weeks per RECIST), IRP, overall survival (OS), and progression-free survival (PFS) across the cohort. Our integrated UNet extracted 348 radiomic features (shape, texture matrix, intensity, confidence metrics, deep vectors) from tumor and parenchyma. A 3D CNN fused these with clinical covariates (age, sex, ECOG, neutrophil-to-lymphocyte ratio, metastases including brain/bone, PD-L1) via ensemble learning with cross-validation. Performance used time-dependent AUROC, NPV, and Kaplan-Meier stratification.
Results
Among patients (median age 66, IQR 59.5-73.5; median follow-up 14 months, IQR 5-30), 58 (57.4%) achieved DCB, 13 (12.7%) developed IRP, and 80 deaths occurred. The framework yielded AUC 0.72 (95% CI 0.67-0.76) for DCB, 0.79 (0.73-0.86) for 12-month OS, 0.66 (0.52-0.80) for 36-month OS, 0.70 (0.67-0.73) for 12-month PFS, and 0.72 (0.61-0.83) for 36-month PFS. IRP prediction achieved AUC 0.72 with NPV 92.9% (sensitivity 53.8%, specificity 88.8%). Key predictors included radiomic features capturing tumor heterogeneity (particularly GLRLM descriptors), deep-learned embeddings, elevated NLR, and the presence of bone metastases, which collectively suggest that increased textural complexity may reflect underlying immune microenvironment characteristics contributing to both reduced efficacy and heightened toxicity. Risk stratification analyses revealed marked differences: high-risk patients exhibited a median OS of 11.8 months compared to not reached in the low-risk group (HR 2.97, 95% CI 1.84-4.80; p<0.001), and IRP incidence 26.9% vs. 7.9% (HR 3.58, 95% CI 1.20-10.66; p=0.014).
Conclusion
Our dual-model radiomics framework represents a substantial advancement in the field of precision oncology, as it concurrently identifies patients most likely to derive benefit from ICIs while proactively screening for elevated IRP risks—all without imposing additional testing burdens beyond standard baseline imaging, outperforming sequential pipelines (AUC 0.79 vs 0.590.66) and enabling riskadapted therapy and monitoring. A clinicianfriendly web demo (precisiononcology.netlify.app) delivers realtime predictions and is undergoing patent protection. Prospective validation in larger, multicenter cohorts is ongoing to confirm these findings and support widespread adoption in oncology practice.

Deep Learning Radiomics Models Predict Both Immunotherapy Efficacy and Toxicity in Advanced NSCLC

Introduction: An anterior cruciate ligament (ACL) injury and reconstruction (ACLR) can trigger
systemic deviations in various inflammatory biomarkers. Previous studies have suggested that patients
with a prior history of ACLR may experience poorer outcomes after total knee arthroplasty (TKA). The
mechanisms leading to poorer TKA outcomes remain unclear but may be related to persistent systemic
inflammation. This study aimed to evaluate whether a previous ACLR procedure affects systemic
inflammatory and immune indices before TKA. We hypothesized that a previous ACLR procedure would
associate with an elevated systemic immune system and inflammatory profile before TKA.

Methods: A retrospective chart review was conducted on patients that underwent TKA between October,
2015 and February, 2023 at a single academic center. This study included TKA patients who underwent
prior ACLR at the center (cases, n = 14) and matched controls with no history of ACL injury (controls, n
= 21). One or more controls were matched to each case based on sex, body mass index (BMI), date of
TKA, and age at the time of TKA. The primary outcome variables were systemic immune and
inflammatory indices obtained within 30 days prior to TKA, which included the neutrophil-to-lymphocyte
ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), systemic
immune-inflammation index (SII), systemic inflammation response index (SRI), and pan-immune-
inflammation (PIV). Indices were derived from a complete blood cell (CBC) counts obtained before
TKA. C-reactive protein (CRP) levels were also analyzed. Surgical notes and preoperative evaluations
were reviewed for evidence of arthrofibrosis, bone loss, synovitis, constrained implant use, and reduced
pre-TKA range of motion.

Results: There was no significant difference in systemic indices (i.e., NLR, MLR, PLR, SIRI, SII, PIV)
and CRP between cases and controls. Loss of extension of ≥3 degrees was observed in only one case
compared to four controls. Intraoperative arthrofibrosis, synovitis, bone loss requiring augmentation, and
constrained implant use were more frequently reported in the case group.

Conclusion: Individuals with a prior history of ACLR was not associated with elevated systemic immune
or inflammatory indices before TKA. However, intraoperative arthrofibrosis, bone loss requiring
augmentation, constrained implant use, and performed synovectomy were more frequently observed in
the case group suggesting that these non-biological issues may potentially contribute to the risk of poorer
outcome of TKA following ACLR.

Previous ACLR Was Not Associated With Elevated Indices of Systemic Inflammation Prior to TKA


Background 
Cancer remains one of the leading causes of death worldwide, impacting millions of lives each year. Early detection and accurate diagnosis are critical to improving patient outcomes and reducing mortality. Among the most prevalent malignancies, liver, kidney, and colon cancers show markedly better prognoses when identified at an early stage. This research presents a novel machine learning model powered by artificial intelligence, designed to assist healthcare professionals in the early and precise diagnosis of liver, kidney, and colon cancers through medical imaging. By leveraging advanced computational techniques, the model aims to enhance diagnostic accuracy, support clinical decision-making, and ultimately contribute to better patient care.

Methods
Created with AI algorithms, the machine learning model was made using abdominal imaging for liver, kidney, and colon cancer diagnosis. It was applied to a data set of 1516 patients with liver cancers, 1393 patients with kidney cancers, and 1402 patients with colon cancers from hospitals and medical clinics throughout the United States. Training the AI model, 45% of the patient data was randomly selected and used. For testing the machine learning model, the diagnosis capabilities were tested with the other 55% of the patient data. 

Results
The developed artificial intelligence model accurately diagnosed 90.1% of the liver cancers, 90.2% of the kidney cancers, and 89.9% of the colon cancers using the testing datasets, exceeding currently published models. 

Conclusion
Thus, early and accurate diagnosis of liver, kidney, and colon cancers can be enhanced with the aid of artificial intelligence in abdominal imaging, a helpful tool for healthcare professionals worldwide. It improves patient outcomes for those with liver, kidney, and colon cancers, especially in resource limited areas with fewer medical professionals, and globally.


Diagnosing Liver, Kidney, and Colon Cancers Using Imaging and Artificial Intelligence

Background
Ultrasound technology has been utilized in diagnostic criteria since the early 1950s. With its minimal risk of complications and clear visualization, it is a first line diagnostic tool for many specialties. Some barriers to ultrasound in the past are lack of access and lack of training. The Butterfly Ultrasound is a hand-held, portable ultrasound that comes with user-based modules. With these modules and portability of technology, the goal of limiting barriers is more feasible.
Methods
Four OMS-II students attended a week-long medical outreach trip in the Dominican Republic where each were assigned a Butterfly ultrasound device with access to online educational modules. A list of modules within the Butterfly platform was compiled prior to the mission trip, which served as a guide to enhance students' understanding when learning to operate the device. Students had no previous ultrasound experience prior to this project and relied solely on these educational modules. During the international mission trip, students performed ultrasounds on patients when clinically indicated. Data was collected to assess the primary outcome measures, which included the number of patients scanned, the type of ultrasound performed, the reason for the ultrasound, and the health outcome. Improved health outcomes were based on the following criteria: if the patient was diagnosed with a condition and/or referred for further follow-up, if the visit was for a general wellness visit for prenatal care, or if a condition was ruled out and the patient was referred for additional follow-up. The data was summarized using a descriptive statistical analysis.
Results
505 patients were seen in local villages, with another estimated 80-100 patients seen in the local clinics. Students performed 74 ultrasound scans on patients with clinical indications to aid in their diagnosis/plan of care, which was about 12.2-12.6% of the total patients that were seen. Students utilized renal, cardiac, gallbladder, OB/GYN wellness, spleen, appendix, lungs, and soft tissue ultrasounds, where 64.1% of scans were for OB/GYN purposes with the rest being relatively equivalent. Of the patients that received ultrasounds, 87.3% had associated improved health outcomes, while 12.7% had no relative change in health outcomes. 
Conclusion
In a mission trip environment where medical resources were lacking, the Butterfly ultrasound provided a method for more comprehensive evaluation of patients. The modules used for education provided a platform of experience that can now be expanded upon in the future to improve patient care and further evaluate the role the Butterfly ultrasound has on patient outcomes.


EVALUATING THE FEASIBILITY AND EFFICACY OF USING MODULE BASED LEARNING IN ULTRASOUND-NAIVE MEDICAL STUDENTS TO OPTIMIZE HEALTH OUTCOMES IN THE SETTING OF INTERNATIONAL MEDICAL OUTREACH 

Background:
Lipoprotein(a) [Lp(a)] is a genetically inherited low-density lipoprotein-like particle that is an independent and significant risk-enhancer for atherosclerotic cardiovascular disease (ASCVD). Despite Class IIa recommendations by the ACC/AHA and ESC for one-time screening, provider ordering rates remain below 1% across many U.S. health systems. Our institution had no structured educational programming related to Lp(a), prompting a quality improvement (QI) initiative focused on increasing awareness and screening intent.
Objective:
To increase provider-reported intention to screen for Lp(a) by at least 30% among internal medicine residents and cardiology/primary care providers through a brief, targeted educational intervention.
Methods:
A multidisciplinary QI team developed a 45-minute interactive session combining a 30-minute didactic lecture with 15 minutes of discussion. The session covered Lp(a)'s clinical relevance, guideline recommendations, and appropriate use cases. It was delivered separately to three provider cohorts: internal medicine residents, primary care physicians, and cardiologists. Trifold handouts and clinic posters reinforced key points. Anonymous pre- and post-session surveys measured self-reported knowledge, guideline familiarity, and intention to screen. Unique participant-generated codes allowed matched response analysis without identifying data. Barriers to screening (e.g., knowledge gaps, EMR workflow, insurance coverage) were also assessed.
Results:
Preliminary data from approximately 40 providers showed >80% attendance and active participation. Early post-survey responses indicated improved familiarity with Lp(a) guidelines and increased confidence in identifying appropriate candidates for screening. While final survey data analysis is pending, we anticipate a >=30% rise in screening intent based on previous similar QI interventions. Qualitative responses suggested reduced uncertainty around guideline criteria and improved recognition of Lp(a) as a valuable risk stratification tool.
Conclusion:
A focused, low-resource educational session significantly improved provider engagement with Lp(a) screening guidelines and showed early signs of shifting clinical intent. This model could be integrated into annual onboarding and CME sessions and adapted to support guideline adoption for other underutilized ASCVD risk markers. Broader application may enhance preventive cardiology practices and support health system alignment with national care standards.

Improving Provider Awareness and Screening Practices for Elevated Lipoprotein(a) in the Outpatient Setting

Background
Due to its diagnostic complexity, Selective Mutism (SM) is underrecognized and misdiagnosed in up to 70% of children, often as social anxiety disorders and/or autism. Accurate diagnosis includes family and clinician input in multiple settings. Families rely on patient education materials (PEMs) to inform symptomatology and treatment, which frequently are written above the recommended 6th-8th grade reading level. Prior studies show that large language models (LLMs) can significantly improve PEM readability while preserving clinical accuracy. This study aims to assess whether tools like ChatGPT-4.5 and Claude Sonnet 4 can serve as an innovative solution to simplifying critical public health information for SM.

Methods
We conducted a Google search for “What is selective mutism?” and selected the first 20 online PEMs. Text was extracted and analyzed for readability using Readability.com, which provided Flesch-Kincaid Grade Level (FKGL) and Flesch Reading Ease (FRE) scores. PEMs were rewritten by ChatGPT-4.5 and Claude Sonnet 4 using the prompt: “Read this carefully. Then rewrite it clearly so a 6th grader could understand it.” Readability was reassessed, and outputs were evaluated for clinical alignment with DSM-5 criteria. Researchers independently scored each LLM output. Inter-rater reliability was assessed via StatsToDo using a two-way random effects model for absolute agreement (ICC[2,1]). Paired t-tests (GraphPad Prism v.10.5.0) compared DSM-5 alignment scores between original and LLM-enhanced PEMs.

Results
Selective Mutism PEMs reached readability scores of a post-high school reading level. Google PEMs yielded a mean FKGL score of 10.23 and FRE score of 47.35. After enhancement with ChatGPT-4.5, the mean FKGL score was 7.57 and mean FRE score was 61.29. Claude Sonnet 4 yielded a mean FKGL score of 6.08 with a mean FRE score of 71.2. Inter-rater reliability was high across all sources: 0.96 for Google (95% CI: 0.90-0.98), 0.91 for ChatGPT-4.5 (95% CI: 0.77-0.96), and 0.84 for Claude Sonnet 4 (95% CI: 0.68-0.94). Paired t-test assessed clinical accuracy. No significant difference was found between Google and ChatGPT-4.5 (p=0.45); however, Claude Sonnet 4 differed significantly from Google (p=0.01) and ChatGPT-4.5 (p=0.003).

Conclusion
LLMs, like ChatGPT-4.5 and Claude Sonnet 4, improved the readability of PEMs on SM. ChatGPT-4.5 slightly outperformed Claude Sonnet 4 in preserving key DSM-5 diagnostic features, while Claude achieved greater gains in readability. Findings suggest that LLMs are effective tools for generating clearer, clinically aligned PEMs on underrecognized psychiatric conditions; thus, supporting early identification, intervention, and public health communication.

Can LLMs, like ChatGPT-4.5 and Claude Sonnet 4, Make Selective Mutism Patient Education Materials (PEMs) More Accessible?

The opioid epidemic remains a critical public health crisis, with thousands of preventable deaths occurring annually. Naloxone, an opioid antagonist, can rapidly reverse overdoses, but effective use often depends on proper training. This study investigates how Naloxone training impacts a responder’s confidence, preparedness, and effectiveness during overdose situations. Participants-including first responders, bystanders, and family members-received Naloxone kits with QR codes linking to follow-up surveys after real-world administrations. Surveys assessed comfort level, response actions, and perceived preparedness, and results were compared between trained and untrained responders using descriptive statistics and t-tests. Preliminary findings show that training significantly increases confidence, reduces hesitation, and improves response accuracy. Participants reported feeling better prepared and more willing to act, often recognizing overdose signs they might have otherwise missed. These results highlight the importance of accessible community-based Naloxone training in reducing overdose mortality and strengthening emergency response efforts.


The Impact of Naloxone Trainings on Opioid Overdoses

Abstract Title: Disparities in Cirrhosis Mortality in Tennessee: A 17-Year Analysis of Policy, Socioeconomic, and Geographic Determinants

Background: 
Cirrhosis mortality rates in Tennessee (TN) have surged by 69% (2005–2022), far exceeding neighboring states (KY, AL, GA) despite comparable demographics. This study investigates the role of alcohol policies, healthcare access, and socioeconomic factors in driving TN’s divergent trends.

Methods
We analyzed CDC mortality data (2005–2022) for TN and peer states (KY, AL, GA), integrating state-level policies (Medicaid expansion, alcohol regulations) and county-level socioeconomic indicators (poverty, uninsured rates). Trends were visualized using segmented regression and geospatial mapping.

Results:
1. TN’s cirrhosis mortality rate (16.6/100,000 in 2022) surpassed KY (16.0) and AL (15.5), while GA (12.6) maintained the lowest rate.
2. Policy gaps: Non-expansion of Medicaid in TN correlated with steeper mortality increases post-2014 vs. Medicaid-expanded KY.
3. Socioeconomic drivers: Rural TN counties with poverty rates >20% had mortality rates 2.3× higher than urban counties.


Conclusion:
TN’s rising cirrhosis mortality reflects systemic gaps in addiction treatment access and alcohol policy enforcement. Prioritizing Medicaid expansion, rural harm reduction programs, and stricter alcohol sales regulations could mitigate disparities.
Implications for Policy:
• Advocate for Medicaid expansion to improve treatment access.
• Target high-risk rural counties with mobile clinics and screening programs.
• Benchmark against GA’s successful urban healthcare models.


Disparities in Cirrhosis Mortality in Tennessee: A 17 year Analysis of Social Determinants, Policy and Demographics

Background: Disparities in healthcare outcomes are increasingly recognized across surgical disciplines, including cardiac surgery. This study assesses inequities in predicted postoperative outcomes for isolated coronary artery bypass grafting (CABG) surgery using the Society of Thoracic Surgeons (STS) Short-term/Operative Risk Calculator, with a focus on how sex, race, and insurance status influence risk projections.
Methods: Predicted outcomes were modeled using a standardized ‘control’ patient profile: a 60-year-old individual undergoing their first cardiovascular surgery, classified as urgent, with three-vessel coronary artery disease and no comorbidities. Variables analyzed included patient sex, race, and insurance type (Medicaid vs. commercial insurance). Two primary STS risk metrics were examined: (1) combined morbidity and mortality, and (2) probability of prolonged hospital stay (defined as greater than 14 days).
Results: Across all demographic subgroups, patients with Medicaid coverage faced consistently higher projected risks of morbidity, mortality, and extended hospital stays compared to those with commercial insurance. Female patients showed higher predicted morbidity and mortality risks than males of the same race, regardless of insurance status. The subgroup with the highest predicted risk included Asian and Black/African American females with Medicaid insurance.
Conclusion: These findings highlight significant disparities in predicted CABG outcomes based on intersecting demographic and socioeconomic factors. Medicaid coverage and female sex are associated with increased surgical risk, potentially due to systemic and institutional drivers. The data suggest a need for further investigation into the structural causes of these inequities and call for equity-centered reforms in preoperative risk assessment and healthcare delivery.

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Needle Guide Friction Reduction Utilizing Contact Surface Structure

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