Hasil untuk "Internal medicine"

R. Miller, H. E. Pople, J. Myers

Dhruv R. Seshadri, Ryan T. Li, J. Voos et al.

The convergence of semiconductor technology, physiology, and predictive health analytics from wearable devices has advanced its clinical and translational utility for sports. The detection and subsequent application of metrics pertinent to and indicative of the physical performance, physiological status, biochemical composition, and mental alertness of the athlete has been shown to reduce the risk of injuries and improve performance and has enabled the development of athlete-centered protocols and treatment plans by team physicians and trainers. Our discussions in this review include commercially available devices, as well as those described in scientific literature to provide an understanding of wearable sensors for sports medicine. The primary objective of this paper is to provide a comprehensive review of the applications of wearable technology for assessing the biomechanical and physiological parameters of the athlete. A secondary objective of this paper is to identify collaborative research opportunities among academic research groups, sports medicine health clinics, and sports team performance programs to further the utility of this technology to assist in the return-to-play for athletes across various sporting domains. A companion paper discusses the use of wearables to monitor the biochemical profile and mental acuity of the athlete.

Juan P Wachs

The CARE Workshop on Robotics and AI in Medicine, held on December 1, 2025 in Indianapolis, convened leading researchers, clinicians, industry innovators, and federal stakeholders to shape a national vision for advancing robotics and artificial intelligence in healthcare. The event highlighted the accelerating need for coordinated research efforts that bridge engineering innovation with real clinical priorities, emphasizing safety, reliability, and translational readiness with an emphasis on the use of robotics and AI to achieve this readiness goal. Across keynotes, panels, and breakout sessions, participants underscored critical gaps in data availability, standardized evaluation methods, regulatory pathways, and workforce training that hinder the deployment of intelligent robotic systems in surgical, diagnostic, rehabilitative, and assistive contexts. Discussions emphasized the transformative potential of AI enabled robotics to improve precision, reduce provider burden, expand access to specialized care, and enhance patient outcomes particularly in undeserved regions and high risk procedural domains. Special attention was given to austere settings, disaster and relief and military settings. The workshop demonstrated broad consensus on the urgency of establishing a national Center for AI and Robotic Excellence in medicine (CARE). Stakeholders identified priority research thrusts including human robot collaboration, trustworthy autonomy, simulation and digital twins, multi modal sensing, and ethical integration of generative AI into clinical workflows. Participants also articulated the need for high quality datasets, shared test beds, autonomous surgical systems, clinically grounded benchmarks, and sustained interdisciplinary training mechanisms.

Eva Kozanli, Wanda Han, Tara Smit et al.

Tianai Huang, Lu Lu, Jiayuan Chen et al.

Large language models (LLMs) excel in various NLP tasks and modern medicine, but their evaluation in traditional Chinese medicine (TCM) is underexplored. To address this, we introduce TCM3CEval, a benchmark assessing LLMs in TCM across three dimensions: core knowledge mastery, classical text understanding, and clinical decision-making. We evaluate diverse models, including international (e.g., GPT-4o), Chinese (e.g., InternLM), and medical-specific (e.g., PLUSE). Results show a performance hierarchy: all models have limitations in specialized subdomains like Meridian & Acupoint theory and Various TCM Schools, revealing gaps between current capabilities and clinical needs. Models with Chinese linguistic and cultural priors perform better in classical text interpretation and clinical reasoning. TCM-3CEval sets a standard for AI evaluation in TCM, offering insights for optimizing LLMs in culturally grounded medical domains. The benchmark is available on Medbench's TCM track, aiming to assess LLMs' TCM capabilities in basic knowledge, classic texts, and clinical decision-making through multidimensional questions and real cases.

Tian Dong, Yan Meng, Shaofeng Li et al.

Large Language Models (LLMs) are increasingly integrated into daily routines, yet they raise significant privacy and safety concerns. Recent research proposes collaborative inference, which outsources the early-layer inference to ensure data locality, and introduces model safety auditing based on inner neuron patterns. Both techniques expose the LLM's Internal States (ISs), which are traditionally considered irreversible to inputs due to optimization challenges and the highly abstract representations in deep layers. In this work, we challenge this assumption by proposing four inversion attacks that significantly improve the semantic similarity and token matching rate of inverted inputs. Specifically, we first develop two white-box optimization-based attacks tailored for low-depth and high-depth ISs. These attacks avoid local minima convergence, a limitation observed in prior work, through a two-phase inversion process. Then, we extend our optimization attack under more practical black-box weight access by leveraging the transferability between the source and the derived LLMs. Additionally, we introduce a generation-based attack that treats inversion as a translation task, employing an inversion model to reconstruct inputs. Extensive evaluation of short and long prompts from medical consulting and coding assistance datasets and 6 LLMs validates the effectiveness of our inversion attacks. Notably, a 4,112-token long medical consulting prompt can be nearly perfectly inverted with 86.88 F1 token matching from the middle layer of Llama-3 model. Finally, we evaluate four practical defenses that we found cannot perfectly prevent ISs inversion and draw conclusions for future mitigation design.

Anbang Du, Michael Head, Markus Brede

Interdisciplinary research is critical for innovation and addressing complex societal issues. We characterise the interdisciplinary knowledge structure of PubMed research articles in medicine as correlation networks of medical concepts and compare the interdisciplinarity of articles between high-ranking (impactful) and less high-ranking (less impactful) medical journals. We found that impactful medical journals tend to publish research that are less interdisciplinary than less impactful journals. Observing that they bridge distant knowledge clusters in the networks, we find that cancer-related research can be seen as one of the main drivers of interdisciplinarity in medical science. Using signed difference networks, we also investigate the clustering of deviations between high and low impact journal correlation networks. We generally find a mild tendency for strong link differences to be adjacent. Furthermore, we find topic clusters of deviations that shift over time. In contrast, topic clusters in the original networks are static over time and can be seen as the core knowledge structure in medicine. Overall, journals and policymakers should encourage initiatives to accommodate interdisciplinarity within the existing infrastructures to maximise the potential patient benefits from IDR.

Malihe Izanloo, Abbas Shamsalinia, Sepide Mohammadi et al.

Background & Aim: Emotional disclosure has been associated with improvements in psychological well-being, immune function, and physical health. This study aimed to evaluate the effect of written emotional disclosure on health behaviors and loneliness among family caregivers of cancer patients. Methods & Materials: This is an experimental study with a pretest–posttest design and a control group. Seventy family caregivers of cancer patients who scored high on the UCLA Loneliness Scale (Version 3) and low on the health behaviors scale for family caregivers of cancer patients were selected by convenience sampling and randomly assigned to Intervention or Control groups. The Intervention group engaged in written emotional disclosure by documenting their deepest and most acute feelings for 15–20 minutes per session, over four consecutive days. The Control group received no intervention. Post-intervention assessments were conducted in both groups. Data were analyzed using SPSS version 26. Comparative analyses included chi-square tests, independent samples t-tests, and paired t-tests. Results: At baseline, there were no significant differences between groups in health behaviors or their components (P>0.05). Post-intervention, the Intervention group demonstrated significantly higher health behaviors scores and component scores than the Control group (P<0.001). Furthermore, the mean loneliness score in the Intervention group decreased significantly after the intervention (P<0.001). Conclusion: Written emotional disclosure has a positive and significant effect on loneliness and health behaviors among family caregivers of cancer patients. Given its ease of learning, potential for unsupervised practice, and minimal need for therapist involvement, this method may be a practical adjunct in caregiver support programs.

V. Innao, A.P.M. Barbagallo, O. Bianco et al.

Introduction: Chronic Lymphocytic Leukemia (CLL) is the most common leukemia in Western countries, with age-related incidence and clinical heterogeneity. Evans Syndrome (ES), defined by the concurrent occurrence of immune thrombocytopenia (ITP) and autoimmune hemolytic anemia (AIHA), is a rare immune disorder often associated with lymphoproliferative diseases and potentially fatal outcomes. Management relies on expert consensus due to the lack of randomized trials. Standard CLL therapies include BTK inhibitors (BTKi) and the BCL2 inhibitor Venetoclax, often with anti-CD20 antibodies. When CLL progression is associated with immune cytopenias, immunosuppressive therapy is used, with CLL-directed treatment in refractory cases. To date, no reports have described ITP onset following Venetoclax in a patient with prior AIHA successfully treated with Zanubrutinib. Patients and Methods: We report the case of a 75-year-old man diagnosed in 2019 with asymptomatic CLL who, in 2021, developed steroid- and IVIG-refractory warm AIHA, treated with Bendamustine-Rituximab, achieving partial response. In July 2024, he showed rapid lymphocytosis progression (DT <2 months), splenomegaly (23 cm), thrombocytopenia (PLT 86×10⁹/L), and constitutional symptoms (Rai IV/Binet C). FISH was negative; IGHV unmutated, TP53 wild-type, CLL-IPI 6. Venetoclax-Rituximab (Murano protocol) was started. In week 2 of ramp-up, the patient developed spontaneous ecchymoses and gingival bleeding. Labs showed PLT 0×10⁹/L, Coombs-positive tests, Hb 10.7 g/dL, and a positive SARS-CoV-2 swab. Steroids and IVIG were ineffective. After swab negativization, Rituximab 375 mg/m² was administered weekly ×4 without response. He was referred to our ITP hub. Results: Romiplostim (Rom) was initiated at 3 μg/kg/week and increased by 2 μg/kg/week; Zanubrutinib (Zanu) 160 mg BID was added in week 2. Platelet and hemoglobin normalization occurred within 4 weeks. After 6 months, BTKi was continued at full dose and Rom reduced to 5 μg/kg/week. A 10-day BTKi interruption led to PLT drop (11×10⁹/L), reversed after Zanu reintroduction. The patient has since maintained a complete hematologic response with no adverse events (fig.1). Conclusion: TPO-RAs are effective in ITP. However, when ITP is secondary to CLL, targeting the underlying disease is crucial. BTKi have shown efficacy in ITP, as highlighted in the LUNA3 trial. This case supports the safety and efficacy of combining BTKi and TPO-RAs in refractory secondary ITP.  

Wasif Khan, Seowung Leem, Kyle B. See et al.

Foundation models (FMs) are large-scale deep learning models trained on massive datasets, often using self-supervised learning techniques. These models serve as a versatile base for a wide range of downstream tasks, including those in medicine and healthcare. FMs have demonstrated remarkable success across multiple healthcare domains. However, existing surveys in this field do not comprehensively cover all areas where FMs have made significant strides. In this survey, we present a comprehensive review of FMs in medicine, focusing on their evolution, learning strategies, flagship models, applications, and associated challenges. We examine how prominent FMs, such as the BERT and GPT families, are transforming various aspects of healthcare, including clinical large language models, medical image analysis, and omics research. Additionally, we provide a detailed taxonomy of FM-enabled healthcare applications, spanning clinical natural language processing, medical computer vision, graph learning, and other biology- and omics- related tasks. Despite the transformative potentials of FMs, they also pose unique challenges. This survey delves into these challenges and highlights open research questions and lessons learned to guide researchers and practitioners. Our goal is to provide valuable insights into the capabilities of FMs in health, facilitating responsible deployment and mitigating associated risks.

Khaled Saab, Tao Tu, Wei-Hung Weng et al.

Excellence in a wide variety of medical applications poses considerable challenges for AI, requiring advanced reasoning, access to up-to-date medical knowledge and understanding of complex multimodal data. Gemini models, with strong general capabilities in multimodal and long-context reasoning, offer exciting possibilities in medicine. Building on these core strengths of Gemini, we introduce Med-Gemini, a family of highly capable multimodal models that are specialized in medicine with the ability to seamlessly use web search, and that can be efficiently tailored to novel modalities using custom encoders. We evaluate Med-Gemini on 14 medical benchmarks, establishing new state-of-the-art (SoTA) performance on 10 of them, and surpass the GPT-4 model family on every benchmark where a direct comparison is viable, often by a wide margin. On the popular MedQA (USMLE) benchmark, our best-performing Med-Gemini model achieves SoTA performance of 91.1% accuracy, using a novel uncertainty-guided search strategy. On 7 multimodal benchmarks including NEJM Image Challenges and MMMU (health & medicine), Med-Gemini improves over GPT-4V by an average relative margin of 44.5%. We demonstrate the effectiveness of Med-Gemini's long-context capabilities through SoTA performance on a needle-in-a-haystack retrieval task from long de-identified health records and medical video question answering, surpassing prior bespoke methods using only in-context learning. Finally, Med-Gemini's performance suggests real-world utility by surpassing human experts on tasks such as medical text summarization, alongside demonstrations of promising potential for multimodal medical dialogue, medical research and education. Taken together, our results offer compelling evidence for Med-Gemini's potential, although further rigorous evaluation will be crucial before real-world deployment in this safety-critical domain.

Usman Ali, Sahil Ranmbail, Muhammad Nadeem et al.

Extracting medication names from handwritten doctor prescriptions is challenging due to the wide variability in handwriting styles and prescription formats. This paper presents a robust method for extracting medicine names using a combination of Mask R-CNN and Transformer-based Optical Character Recognition (TrOCR) with Multi-Head Attention and Positional Embeddings. A novel dataset, featuring diverse handwritten prescriptions from various regions of Pakistan, was utilized to fine-tune the model on different handwriting styles. The Mask R-CNN model segments the prescription images to focus on the medicinal sections, while the TrOCR model, enhanced by Multi-Head Attention and Positional Embeddings, transcribes the isolated text. The transcribed text is then matched against a pre-existing database for accurate identification. The proposed approach achieved a character error rate (CER) of 1.4% on standard benchmarks, highlighting its potential as a reliable and efficient tool for automating medicine name extraction.

Idoia Berges, Jesús Bermúdez, Arantza Illarramendi

Introduction: This article is part of the Focus Theme of METHODS of Information in Medicine on "Managing Interoperability and Complexity in Health Systems". Background: The proliferation of archetypes as a means to represent information of Electronic Health Records has raised the need of binding terminological codes - such as SNOMED CT codes - to their elements, in order to identify them univocally. However, the large size of the terminologies makes it difficult to perform this task manually. Objectives: To establish a baseline of results for the aforementioned problem by using off-the-shelf string comparison-based techniques against which results from more complex techniques could be evaluated. Methods: Nine Typed Comparison METHODS were evaluated for binding using a set of 487 archetype elements. Their recall was calculated and Friedman and Nemenyi tests were applied in order to assess whether any of the methods outperformed the others. Results: Using the qGrams method along with the 'Text' information piece of archetype elements outperforms the other methods if a level of confidence of 90% is considered. A recall of 25.26% is obtained if just one SNOMED CT term is retrieved for each archetype element. This recall rises to 50.51% and 75.56% if 10 and 100 elements are retrieved respectively, that being a reduction of more than 99.99% on the SNOMED CT code set. Conclusions: The baseline has been established following the above-mentioned results. Moreover, it has been observed that although string comparison-based methods do not outperform more sophisticated techniques, they still can be an alternative for providing a reduced set of candidate terms for each archetype element from which the ultimate term can be chosen later in the more-than-likely manual supervision task.

Thomas Savage, Stephen Ma, Abdessalem Boukil et al.

Large Language Model (LLM) fine tuning is underutilized in the field of medicine. Two of the most common methods of fine tuning are Supervised Fine Tuning (SFT) and Direct Preference Optimization (DPO), but there is little guidance informing users when to use either technique. In this investigation, we compare the performance of SFT and DPO for five common natural language tasks in medicine: Classification with text data, Classification with numeric data, Clinical Reasoning, Summarization, and Clinical Triage. We find that SFT alone is sufficient for Classification with text data, whereas DPO improves performance for the more complex tasks of Clinical Reasoning, Summarization and Clinical Triage. Our results establish the role and importance of DPO fine tuning within medicine, and consequently call attention to current software gaps that prevent widespread deployment of this technique.

Aylin Bayındır-Gümüş, Ebru Öztürk, Mihály Soós

Background. People live in a technological world, where social media is used very commonly. Social media has effects on eating behaviors, as in other aspects. For this reason, it is important to measure social media effect. Objective. This study aimed to adapt the Scale of Effects of Social Media on Eating Behaviour (SESMEB) that examines the effect of social media on eating behavior in Hungarian university students. Material and methods. The SESMEB was translated into the target language by taking various stages. The online questionnaire including general information, social media use, and the eighteen-item SESMEB was used to collect data. The scale was administered to the study group consisting of 213 Hungarian university students, and data from 203 of them were analyzed. Confirmatory factor analyses were performed to test construct validity, and the Cronbach alpha coefficient was calculated for the reliability of the scale in Hungarian. Results. Total correlation value was higher than 0.50 for all items of the scale. The fit indices were at an acceptable level or had a perfect fit. The t-values were significant at the level of 0.1 and ranged between 2.927 and 5.706. The Spearman–Brown coefficient was calculated at 0.894. The reliability coefficient of the scale was calculated to be 0.866. SESMEB scores were different according to spending time daily, sharing content, and using filters or Photoshop on social media (p < 0.05). Conclusions. Higher than 0.80 Cronbach’s alpha coefficient and other results show that Hungarian SESMEB is a valid and reliable tool. Therefore, Hungarian SESMEB will be useful for further studies to determine the impact of social media on eating behaviors.

Md Fahim Anjum, Arturo I. Espinoza, Rachel C. Cole et al.

Abstract Cognitive dysfunction is common in Parkinson’s disease (PD). We developed and evaluated an EEG-based biomarker to index cognitive functions in PD from a few minutes of resting-state EEG. We hypothesized that synchronous changes in EEG across the power spectrum can measure cognition. We optimized a data-driven algorithm to efficiently capture these changes and index cognitive function in 100 PD and 49 control participants. We compared our EEG-based cognitive index with the Montreal cognitive assessment (MoCA) and cognitive tests across different domains from National Institutes of Health (NIH) Toolbox using cross-validations, regression models, and randomization tests. Finally, we externally validated our approach on 32 PD participants. We observed cognition-related changes in EEG over multiple spectral rhythms. Utilizing only 8 best-performing electrodes, our proposed index strongly correlated with cognition (MoCA: rho = 0.68, p value < 0.001; NIH-Toolbox cognitive tests: rho ≥ 0.56, p value < 0.001) outperforming traditional spectral markers (rho = −0.30–0.37). The index showed a strong fit in regression models (R 2 = 0.46) with MoCA, yielded 80% accuracy in detecting cognitive impairment, and was effective in both PD and control participants. Notably, our approach was equally effective (rho = 0.68, p value < 0.001; MoCA) in out-of-sample testing. In summary, we introduced a computationally efficient data-driven approach for cross-domain cognition indexing using fewer than 10 EEG electrodes, potentially compatible with dynamic therapies like closed-loop neurostimulation. These results will inform next-generation neurophysiological biomarkers for monitoring cognition in PD and other neurological diseases.

Dachen Luo, Dongmei Yang, Dan Cao et al.

BackgroundRecent studies have yielded conflicting results regarding the relationship between smoking history and the effectiveness of immune checkpoint inhibitors (ICIs) for advanced lung cancer. While some studies have suggested that smoking may enhance the response to immunotherapy in patients with lung cancer, other findings indicate the contrary. Therefore, we conducted a systematic review and meta-analysis to thoroughly examine this association.MethodsWe searched the PubMed, Embase, and Scopus databases for clinical trials comparing immunotherapy with conventional chemotherapy as the primary treatment for advanced lung cancer. A random effects model was used to synthesize hazard ratios (HRs) and 95% confidence intervals (CIs) for overall survival (OS). We also conducted predefined subgroup analyses to investigate the efficacy disparities between never-smokers and smokers who were administered immunotherapy alone or in combination with chemotherapy, as well as the differences between former and current smokers under similar treatment modalities.ResultsOur analysis included data from 17 Phase III clinical trials involving 10,283 patients. The findings indicate that immunotherapy benefits both smokers and never-smokers with lung cancer or non-small cell lung cancer, yielding pooled HRs for OS of 0.74 (95% CI: 0.59–0.92) and 0.73 (95% CI: 0.67–0.80), respectively. A significant interaction effect was not observed (HR: 0.98, 95% CI: 0.77–1.24, pinteraction = 0.14), and the tumor type, immunotherapy combination, and type of immunotherapy did not differ among the groups in the subgroup analyses. Similarly, both former and current smokers experienced a significant survival benefit from immunotherapy, with pooled HRs for OS of 0.79 (95% CI: 0.68–0.91) and 0.71 (95% CI: 0.59–0.87), respectively. However, a significant interaction effect was also not observed (HR: 0.91, 95% CI: 0.74–1.11, pinteraction = 0.14).ConclusionOur findings suggest that smoking status does not affect the effectiveness of immunotherapy for lung cancer treatment. However, additional high-quality clinical trials are needed to confirm this conclusion.Systematic review registrationhttps://inplasy.com/register/, identifier INPLASY2023110058.

Haider Sultan, Hafiza Farwa Mahmood, Noor Fatima et al.

Like other fields of Traditional Medicines, Unani Medicines have been found as an effective medical practice for ages. It is still widely used in the subcontinent, particularly in Pakistan and India. However, Unani Medicines Practitioners are lacking modern IT applications in their everyday clinical practices. An Online Clinical Decision Support System may address this challenge to assist apprentice Unani Medicines practitioners in their diagnostic processes. The proposed system provides a web-based interface to enter the patient's symptoms, which are then automatically analyzed by our system to generate a list of probable diseases. The system allows practitioners to choose the most likely disease and inform patients about the associated treatment options remotely. The system consists of three modules: an Online Clinical Decision Support System, an Artificial Intelligence Inference Engine, and a comprehensive Unani Medicines Database. The system employs advanced AI techniques such as Decision Trees, Deep Learning, and Natural Language Processing. For system development, the project team used a technology stack that includes React, FastAPI, and MySQL. Data and functionality of the application is exposed using APIs for integration and extension with similar domain applications. The novelty of the project is that it addresses the challenge of diagnosing diseases accurately and efficiently in the context of Unani Medicines principles. By leveraging the power of technology, the proposed Clinical Decision Support System has the potential to ease access to healthcare services and information, reduce cost, boost practitioner and patient satisfaction, improve speed and accuracy of the diagnostic process, and provide effective treatments remotely. The application will be useful for Unani Medicines Practitioners, Patients, Government Drug Regulators, Software Developers, and Medical Researchers.

Krystian Strzałka, Szymon Mazurek, Maciej Wielgosz et al.

This paper explores the innovative use of simulation environments to enhance data acquisition and diagnostics in veterinary medicine, focusing specifically on gait analysis in dogs. The study harnesses the power of Blender and the Blenderproc library to generate synthetic datasets that reflect diverse anatomical, environmental, and behavioral conditions. The generated data, represented in graph form and standardized for optimal analysis, is utilized to train machine learning algorithms for identifying normal and abnormal gaits. Two distinct datasets with varying degrees of camera angle granularity are created to further investigate the influence of camera perspective on model accuracy. Preliminary results suggest that this simulation-based approach holds promise for advancing veterinary diagnostics by enabling more precise data acquisition and more effective machine learning models. By integrating synthetic and real-world patient data, the study lays a robust foundation for improving overall effectiveness and efficiency in veterinary medicine.

Michael M.H. Yang, MD, MSc, MBiotech, Jay Riva-Cambrin, MD, MSc, Jonathan Cunningham, MD, MSc et al.

Background: Approximately 30% to 64% of patients experience inadequate pain control following spine surgery. The Calgary postoperative pain after spine surgery (CAPPS) score was developed to identify this subset of patients. The impact of preoperative insomnia on postoperative pain control is unknown. This study aimed to investigate the relationship between preoperative insomnia and poor pain control after spine surgery, as well as improve the predictive accuracy of the CAPPS score. Methods: A prospective cohort study was conducted in patients undergoing elective spine surgery. Poor pain control was defined as a mean numeric rating scale pain score >4 at rest within the first 24-hours after surgery. Patients were evaluated using the CAPPS score, which included 7 prognostic factors. A multivariable logistic regression model was used to examine the association between preoperative insomnia severity index (ISI) and poor pain control, adjusting for the CAPPS score. The Modified CAPPS score was derived from this model. Results: Of 219 patients, 49.7% experienced poorly controlled pain. Prevalence of clinical insomnia (ISI≥15) was 26.9%. Preoperative ISI was independently associated with poor pain control (odds ratio [OR] 1.09, [95%CI=1.03–1.16], p=.004), after adjusting for the CAPPS score (OR 1.61, [95%CI=1.38–1.89], p<.001). The model exhibited good discrimination (c-statistics 0.80, [95%CI=0.74–0.86]) and calibration (Hosmer-Lemeshow chi-square=8.95, p=.35). The Modified CAPPS score also demonstrated good discrimination (c-statistic 0.78, [95%CI=0.72–0.84]) and calibration (Hosmer-Lemeshow chi-square=2.92, p=.57). Low-, high-, and extreme-risk groups stratified by the Modified CAPPS score had 17.3%, 49.1%, and 80.7% predicted probability of experiencing inadequate pain control compared to 32.0%, 64.0%, and 85.1% in the CAPPS score. Conclusions: Preoperative insomnia is prevalent and is a modifiable risk factor for poor pain control following spine surgery. Early identification and management of preoperative insomnia may lead to improved postoperative pain outcomes. Future external validation is needed to confirm the accuracy of the Modified CAPPS score.