Search Results (474)

Background: Stroke–heart syndrome (SHS), particularly acute myocardial injury, is a critical complication following acute ischemic stroke (AIS). The C-reactive protein–triglyceride–glucose index (CTI) integrates inflammatory and metabolic parameters but remains unexplored in the context of post-stroke cardiac complications. This study investigated whether CTI predicts cardiac injury patterns and 90-day clinical outcomes in AIS patients. Methods: A two-center retrospective cohort study was conducted in AIS patients undergoing endovascular treatment (EVT). Cardiac troponin I (cTnI) trajectories were classified into: no injury, non-dynamic elevation, and dynamic elevation. The primary endpoint was poor functional status at 90 days (modified Rankin Scale [mRS] 3–6); the secondary endpoint was 90-day all-cause death. Results: Among 493 individuals (median age 69 years, 42% female), higher baseline CTI was associated with a greater likelihood of dynamic troponin elevation (adjusted odds ratio [aOR] per 1-unit increase = 1.56 (1.26–1.94); p < 0.001). Patients with dynamic elevation had significantly worse outcomes compared to those with no injury. Elevated CTI was an independent predictor of 90-day poor functional outcome (Q4: aOR = 3.04 (1.53–6.05); p < 0.001) and mortality (Q4: aOR = 2.82 (1.33–6.00); p = 0.007). Conclusions: In EVT-treated AIS patients, the CTI is a predictor of SHS and adverse 90-day outcomes. This easily calculable index may help identify individuals at higher risk of cardiac complications after AIS. Full article

Healthcare artificial intelligence (AI) is moving from the laboratory into the infrastructure of care. As these systems become embedded in imaging, electronic health records, triage, and clinical decision support, their failures can affect not only individual encounters but also institutions and patient populations. Yet governance still centers on model development, local validation, and one-time compliance, with limited attention to cross-site failure after deployment. This article examines how public health preparedness can help close that gap. It presents a conceptual analysis grounded in two cases: a pneumonia-screening convolutional neural network that learned institutional confounders rather than portable clinical signals, and a widely deployed sepsis prediction model whose external performance and alert burden fell short of developer claims. Together, these cases reveal five governance features of systemic healthcare AI risk: population-level exposure, cascade effects across shared infrastructures, unequal vulnerability, delayed recognition, and coordination needs beyond any single institution. In response, we propose a tripartite framework combining stronger pre-deployment assurance, post-deployment surveillance with escalation thresholds, and tertiary response through investigation, rollback, remediation, and cross-site learning. The argument is not that AI failures are epidemics, but that high-impact clinical AI systems now function as critical digital health infrastructure requiring preparedness alongside lifecycle oversight. Full article

Background: Minimally invasive interventional radiology (IR) offers effective, uterus-preserving treatments for several gynecologic and obstetric conditions such as uterine fibroids, adenomyosis and postpartum hemorrhage. Despite their efficacy, these methods remain underused, partly to limited awareness among clinicians and patients. Large language models (LLMs) may help bridge this gap by providing accessible, reliable information. Objective: To evaluate how current LLMs address knowledge gaps and promote awareness of minimally invasive IR methods in gynecology and obstetrics. Methods: A structured ten-question instrument was used to query three publicly available LLMs (OpenEvidence, ChatGPT, and Google Gemini). Responses were analyzed for accuracy, completeness, safety considerations, and patient-centered communication. Results: All three models accurately identified a range of medical, minimally invasive, and surgical treatments for uterine fibroids, adenomyosis, and postpartum hemorrhage, with OpenEvidence and ChatGPT providing more detailed and clinically nuanced responses. OpenEvidence achieved the highest scores overall, closely followed by ChatGPT, while Google Gemini scored lower, particularly in completeness and patient-centered communication. In more complex scenarios, performance differences became more pronounced, with OpenEvidence again leading, ChatGPT performing strongly, and Google Gemini lagging behind. Overall, OpenEvidence and ChatGPT demonstrated higher accuracy, completeness, and safety considerations, whereas Google Gemini showed comparatively weaker and less consistent performance. Conclusions: LLMs may endorse the promotion of minimally invasive IR methods in gynecology and obstetrics, but their outputs vary considerably in quality. Ongoing refinement and integration of evidence-based sources are essential before routine use in clinical practice. Therefore, effective collaboration between artificial intelligence (AI) developers and medical professionals is essential to harness this technology’s full potential. Full article

Background: Posterior spinal fusion (PSF) for adolescent idiopathic scoliosis (AIS) is a standard procedure with recognized long-term complications that may emerge years after surgery. Informed consent requires disclosure of material risks, but it is unclear whether these long-term sequelae are consistently communicated. This study systematically reviewed publicly available consent materials to assess disclosure of evidence-based long-term complications of PSF for AIS. Methods: Official websites of spine, orthopedic, and neurosurgical societies, along with major hospitals across North America, South America, Europe, and Australia, were searched for publicly available informed consent forms and patient information leaflets related to PSF for AIS. Documents were assessed for explicit mention of predefined long-term complications: chronic pain/health-related quality of life, pseudoarthrosis, adjacent segment degeneration, future surgery, pulmonary function impact, late infection, local tissue reaction to metal debris, and pregnancy-related issues. Disclosure frequencies were calculated. Results: Thirty-one documents from ten countries were included. Immediate perioperative risks were almost universally reported, whereas long-term complications were inconsistently disclosed. Reporting frequencies were: pseudoarthrosis, 80.6% (n = 25); future surgery, 67.7% (n = 21); adjacent segment degeneration, 51.6% (n = 16); chronic pain, 48.4% (n = 15); local tissue reaction to metal debris, 38.7% (n = 12); late infection, 25.8% (n = 8); pregnancy-related issues, 22.6% (n = 7); and pulmonary impact, 9.7% (n = 3). Conclusions: Publicly available consent materials for AIS surgery incompletely disclose long-term complications compared with the published evidence. However, written information sheets and consent forms represent only one component of the consent process. Consistently with the patient-centered standard articulated in Montgomery v Lanarkshire Health Board, informed consent should include discussion of material risks, benefits, reasonable alternative treatments including standard care, and the option of no treatment, with disclosure tailored to what matters to the patient and family. Updating written materials to better reflect lifelong risks may strengthen one important component of informed consent and shared decision-making for patients and families. Full article

Background/Objectives: Electroanatomic mapping (EAM) provides high-resolution spatial and electrogram information, but the prognostic utility of quantitative EAM features has not been systematically evaluated with contemporary artificial intelligence (AI) methods. We investigated whether an AI analysis of quantitative EAM exports from the CARTO system enhances the prediction of major arrhythmic events (MAEs). Methods: In this retrospective, multicenter cohort study, 248 consecutive patients undergoing left ventricular EAM at four tertiary electrophysiology centers were analyzed. Numerical EAM descriptors (spatial coordinates, unipolar/bipolar voltages, local activation time, impedance) were transformed into derived metrics, including local activation heterogeneity (GR), late-potential extent (LAT), bipolar–unipolar discrepancy (VLT), and low-amplitude scar extent (Scar Areas), and were spatially normalized via spherical projection. Clinical, anamnestic, and imaging variables were integrated. Machine learning and deep learning models were trained with an 80:20 train/test split and evaluated using three-fold cross-validation. Performance metrics included area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and precision. Results: Models incorporating both clinical and AI-processed EAM features achieved high discriminatory performance (test AUC up to 0.92; accuracy up to 0.896). Specificity was consistently high (≈0.97–0.998), whereas sensitivity remained modest (≈0.39–0.58). Among the EAM-derived features, GR was the most consistently informative predictor across algorithms and analyses; VLT, LAT, and Scar Areas also contributed substantially. Regionally, basal sub-mitral, subaortic, and posterolateral basal-to-mid zones exhibited the strongest associations with MAEs. Conclusions: AI-driven quantitative analysis of left ventricular EAM exports augments risk stratification for MAEs beyond conventional clinical and binary EAM descriptors. Reflecting local conduction heterogeneity, GR emerged as the dominant EAM predictor. Prospective validation in larger, disease-specific cohorts and real-time integration within EAM platforms are warranted. Full article

Background: Magnetic resonance imaging (MRI) is the reference standard for evaluating temporomandibular joint (TMJ) disorders, particularly for assessing disc position, joint effusion, and degenerative changes. With increasing imaging demands and advances in deep learning, artificial intelligence (AI) has emerged as a potential adjunct to expert interpretation. This systematic review aimed to compare the diagnostic performance of AI-based models with that of human experts in TMJ MRI analysis. Methods: This review was conducted in accordance with the PRISMA 2020 guidelines and prospectively registered in PROSPERO (CRD420251174127). A systematic search of PubMed/MEDLINE, ScienceDirect, Wiley Online Library, and Springer Nature Link was performed for studies published between 2020 and 2026. Eligible studies included human participants undergoing TMJ MRI and evaluated AI, machine learning, or deep learning models against human expert interpretation. Extracted outcomes included sensitivity, specificity, accuracy, area under the receiver operating characteristic curve (AUC), and agreement metrics. Risk of bias was assessed using QUADAS-2. Due to substantial heterogeneity, a narrative synthesis was conducted. Results: Five retrospective diagnostic accuracy studies were included, comprising sample sizes ranging from 118 to 1474 patients. Target conditions included anterior disc displacement, joint effusion, osteoarthritis, and disc perforation. AI models demonstrated strong discriminative performance, with reported AUC values ranging from 0.79 to 0.98. In direct comparisons, AI achieved diagnostic accuracy comparable to experienced radiologists. AI systems frequently demonstrated higher specificity and similar overall accuracy, whereas human experts often showed higher sensitivity. In osteoarthritis assessment, AI performance approached expert level and exceeded that of less experienced readers. All studies were retrospective and predominantly single-center, with heterogeneous reference standards and limited external validation. Conclusions: AI achieves diagnostic performance comparable to experienced clinicians in TMJ MRI interpretation and shows promise as a decision-support tool. Nevertheless, it should be regarded as complementary to, rather than a replacement for, expert radiological assessment pending further rigorous validation. Full article

Background: Artificial intelligence (AI) is progressively entering perioperative medicine; however, its role in preoperative anesthetic decision-making remains insufficiently characterized. We evaluated the concordance between anesthesiologist-selected anesthetic techniques and algorithm-generated recommendations in a cohort of adult patients undergoing non-cardiac surgery. Methods: This retrospective observational study included adult patients (≥18 years) undergoing elective non-cardiac surgery between January 2024 and January 2025 at two international centers (Mexico and Italy). Clinical, demographic, and surgical variables were extracted from electronic medical records. For each case, a structured anonymized vignette was submitted to ChatGPT (version 5.0, medical configuration) to obtain an independent recommendation regarding anesthetic technique. Concordance between AI-generated and clinician-selected techniques was assessed using agreement analysis and stratified by country and surgical specialty. Results: A total of 1965 patients were analyzed. Overall concordance between ChatGPT recommendations and anesthesiologist-selected techniques was 84.6%. Agreement remained stable across centers (Mexico 84.3%; Italy 88.7%). Disagreement rates varied by surgical specialty, with the highest values observed in vascular and proctologic surgery (28.6%), followed by urology (21.1%) and thoracic surgery (18.8%). Orthopedic procedures—particularly shoulder arthroscopy—accounted for a relevant proportion of divergences, where AI frequently favored regional techniques over general anesthesia. No specialty demonstrated discordance exceeding 30%. Conclusions: AI-generated anesthetic recommendations demonstrated substantial concordance with expert clinical decision-making across heterogeneous surgical settings. These findings support the potential integration of AI within a hybrid decision-making framework, complementing—rather than replacing—anesthesiologist expertise in contemporary perioperative care. Full article

Background and Objectives: Enhanced Recovery After Surgery (ERAS) pathways are increasingly used in spine surgery, but uptake in adolescent idiopathic scoliosis (AIS) remains heterogeneous across institutions. Evidence in pediatric deformity surgery supports shorter recovery with protocolized care, yet real-world comparative data combining ERAS and the erector spinae plane block (ESPB) remain limited. This study aimed to compare early postoperative outcomes between a historical standard-care pathway and a structured ERAS+ESPB pathway in adolescents undergoing posterior spinal fusion for AIS. Materials and Methods: A single-center retrospective time-based comparative cohort study design included consecutive AIS patients (<18 years) treated between 1 January 2024 and 31 December 2025. The standard-care pathway was applied to patients operated on before 1 June 2025 (n = 34), whereas the ERAS+ESPB pathway was applied to those operated on from 1 June 2025 onward (n = 35), following formal institutional implementation. Outcomes included postoperative pain assessed using the visual analog scale under two functional conditions—at rest in the supine position and during standing/mobilization—at POD0, POD1, POD2, POD3, discharge, and 2-week follow-up; postoperative nausea at POD0–POD3; and length of stay (LOS). Between-group pain comparisons used Welch’s t-test; nausea used Fisher’s exact test; LOS used the Wilcoxon rank-sum test. Results: At POD0, supine pain was lower in ERAS+ESPB (1.50 ± 0.55) than in standard care (3.20 ± 1.50; p < 0.001). From POD1 onward, supine pain did not differ significantly between groups. Among assessable patients, standing pain was lower in ERAS+ESPB at POD2 (3.05 ± 1.53 vs. 4.50 ± 1.05; p = 0.020), POD3 (2.82 ± 1.62 vs. 4.17 ± 1.03; p = 0.006), and 2-week follow-up (1.45 ± 0.80 vs. 2.26 ± 0.93; p = 0.006). Nausea was lower in ERAS+ESPB at POD0 (11.4% vs. 35.3%; p = 0.024) and POD2 (8.6% vs. 32.4%; p = 0.018), with no significant differences at POD1 or POD3. LOS was shorter in ERAS+ESPB (5.41 ± 1.10 vs. 8.32 ± 2.06 nights; p < 0.001). Conclusions: In adolescents undergoing posterior spinal fusion for AIS, an ERAS-based perioperative pathway incorporating ESPB was associated with improved early postoperative recovery, particularly in terms of immediate postoperative pain, pain during mobilization, early postoperative nausea at selected time points, and length of hospital stay. Prospective multicenter studies are needed to confirm these findings and clarify the independent contribution of individual pathway components. Full article

Background/Objectives: Stroke generates 157 million disability-adjusted life-years (DALYs) annually, making it the leading neurological cause of global disease burden. Artificial intelligence (AI) and machine learning (ML) have emerged as transformative technologies across the stroke care continuum. This narrative review maps the trajectory of AI in stroke medicine over the decade from 2015 to 2025. Methods: We conducted a narrative review with a structured, pre-specified search strategy across eight pre-specified thematic clusters using PubMed/MEDLINE (January 2015–December 2025), identifying 8549 records and including 1335 studies after screening. Inclusion criteria encompassed primary research articles, systematic reviews, meta-analyses, and RCTs reporting quantitative performance metrics or clinical outcome data for AI/ML in stroke. Results: Stroke imaging AI is the most commercially mature domain, with over 30 FDA-cleared tools. Automated ASPECTS scoring reduced radiologist reading time by 74.8% (AUC 84.97%; 95% CI: 83.1–86.8%). The only triage AI RCT demonstrated an 11.2 min reduction in door-to-groin time without significant improvement in 90-day functional independence (OR 1.3, 95% CI 0.42–4.0). Brain–computer interface rehabilitation showed significant upper limb recovery in a 17-center RCT (FMA-UE mean difference +3.35 points, 95% CI 1.05–5.65; p = 0.0045). AF detection AI is FDA-cleared and RCT-validated. LLMs and federated learning are pre-regulatory but growing exponentially. Conclusions: AI in stroke has achieved diagnostic maturity but therapeutic immaturity. Bridging algorithmic performance to patient outcomes, addressing equity gaps, and building the economic evidence base for scalable deployment are the defining challenges of the next decade. Full article

Morocco, facing a growing prevalence of chronic diseases such as diabetes, hypertension, and cardiovascular diseases, must overcome significant challenges to modernize its healthcare system. In this context, the integration of digital technologies, including telemedicine, the Internet of Medical Things (IoMT), Artificial Intelligence (AI), and healthcare system interoperability, represents a promising solution to improve the management of chronic diseases. This article examines how these technologies can be utilized to transform the Moroccan healthcare system into a more accessible, efficient, and patient-focused model of care. The paper reviews recent pilot projects and initiatives, focusing on infrastructure development, remote monitoring, AI and IoMT integration, public health campaigns, and national health programs aimed at improving access to treatment. Building on these observations, the paper explores the potential of an integrated digital health system for managing chronic diseases and proposes a national integrated care architecture that connects Morocco’s public and private healthcare providers. These insights highlight the significance of digital health in Morocco and provide a framework for improved, more patient-centered, and more efficient advanced healthcare. Future perspectives focus on developing an adapted digital transformation approach to further enhance chronic disease management. Full article

Personalized transdermal drug delivery systems (TDDS) represent a transformative approach in precision medicine by enabling patient-specific, non-invasive, and controlled therapeutic administration. Conventional transdermal patches are limited by fixed dosing, passive diffusion, and interindividual variability in skin permeability and metabolism, often leading to suboptimal therapeutic outcomes. Recent advances in materials science, nanotechnology, microneedle engineering, and digital health have enabled the development of next-generation personalized TDDS capable of programmable, adaptive, and feedback-controlled drug release. Smart wearable patches integrating biosensors, microfluidics, microneedles, and wireless connectivity allow real-time monitoring of physiological and biochemical parameters, enabling closed-loop drug delivery tailored to individual metabolic profiles. Nanocarriers such as lipid nanoparticles, polymeric nanoparticles, and stimuli-responsive hydrogels further enhance drug stability, penetration, and controlled release, while 3D-printing technologies facilitate patient-specific customization of patch geometry, drug loading, and release kinetics. Artificial intelligence (AI) and machine learning tools are increasingly being employed to predict drug permeation behavior, optimize enhancer combinations, and personalize dosing regimens based on pharmacogenomic and pharmacokinetic data. Despite these advances, regulatory complexity, manufacturing standardization, long-term biocompatibility, and cybersecurity considerations remain critical challenges for clinical translation. This review highlights recent innovations in personalized TDDS, discusses their clinical potential, and examines regulatory and technological barriers. Collectively, these emerging smart transdermal platforms offer a promising pathway toward adaptive, patient-centered therapeutics that can significantly improve treatment efficacy, safety, and compliance. Future research should focus on integrating multimodal biosensing, advanced biomaterials, scalable manufacturing strategies, and robust regulatory frameworks to enable clinically validated, fully autonomous transdermal systems that can dynamically adapt to real-time patient needs in diverse therapeutic settings. Full article

Background/Objectives: Time-constrained consultations in high-volume settings can crowd out patient-centered communication, while AI-generated advice may face algorithm aversion when it lacks a humanistic dimension. This study examined whether a brief narrative-based prompt could improve coded patient-facing communication features in an LLM relative to both clinicians and an unprompted model on authentic patient queries. Methods: We conducted a three-condition comparative evaluation using a stratified sample of 1000 de-identified MedDialog-CN consultations (2016–2020). For each consultation, the same patient query was used to generate (i) a zero-shot GPT-o3-mini response and (ii) a narrative-prompted GPT-o3-mini response; the original physician reply served as the human baseline. Responses were annotated with a pre-specified schema operationalizing four communication dimensions—Storytelling, Empathy, Personalization, and Clarity—with expert adjudication. Frequency-based indicators were summarized as mean events per consultation, and binary indicators as proportions; secondary checks captured unwarranted certainty and risk-relevant language. Results: Narrative prompting shifted coded patient-facing communication from sparse and selectively deployed (clinicians and zero-shot AI) to more routine and standardized. Across the reported communication measures, the prompted model showed the most favorable overall pattern, with higher narrative-device use, empathic support, contextual tailoring, and terminology explanation, alongside more frequent consideration of patient preferences and markedly higher rates of emotion–symptom linkage and the presence of a patient-centered narrative framework. Conclusions: Narrative prompting may offer a lightweight and potentially scalable strategy for improving patient-facing communication in Chinese asynchronous, text-based online consultations. An important next step is calibration: humanistic cues should be delivered selectively and safely so that responses remain credible, locally feasible, and cognitively manageable. Full article

Background/Objectives: Rapid and accurate electrocardiogram (ECG) interpretation is essential for timely recognition of ST-elevation myocardial infarction (STEMI) and initiation of reperfusion therapy in the emergency department (ED). We evaluated the diagnostic performance of a real-time artificial intelligence (AI) ECG interpretation system and its pragmatic impact when integrated into routine ED workflows. Methods: This prospective, single-center pragmatic observational study was conducted in a regional emergency medical center ED in Busan, Republic of Korea (1 January–31 December 2024). Consecutive adults (≥18 years) undergoing 12-lead ECG for cardiovascular-related symptoms were enrolled (N = 1524). A predefined alternating-day protocol allocated visits to physician-only interpretation days (physician-days, N = 763) or AI output disclosure days (AI-days, N = 761). Diagnostic performance for STEMI was assessed using paired ECG-level comparisons between physician-alone interpretation and AI output against a blinded expert-panel reference standard; clinical impact outcomes included reperfusion-related time metrics, hospital length of stay (LOS), and in-hospital mortality. Results: Against the expert reference standard, AI showed higher STEMI sensitivity than physician-alone interpretation (96.7% vs. 68.3%; McNemar p = 0.027), while specificity was lower (75.9% vs. 84.5%; p = 0.018). In pragmatic day-level comparisons, door-to-balloon time was shorter on AI-days (40.0 ± 19.81 vs. 47.34 ± 21.90 min; p = 0.001), and time to PCI was significantly reduced among patients with atypical presentations (42.3 ± 18.21 vs. 57.1 ± 20.11 min; p = 0.013). Among admitted patients, hospital LOS was shorter on AI-days (13 ± 9.21 vs. 17 ± 10.31 days; p = 0.010), whereas in-hospital mortality did not differ significantly between groups (17.0% vs. 16.77%; p = 0.191). Conclusions: Real-time AI-ECG integration in the ED was associated with improved STEMI detection sensitivity and shorter reperfusion-related time metrics, particularly in atypical presentations, and with reduced hospital LOS among admitted patients. Short-term mortality was comparable between groups. Further multicenter studies are warranted to confirm generalizability and to balance benefits against potential false-positive-related operational impacts. Full article

Artificial intelligence (AI) is rapidly reshaping head and neck surgical oncology by augmenting decision-making across the full perioperative continuum. This state-of-the-art review aims to provide head and neck surgical oncologists with a conceptual framework for understanding and critically appraising AI tools entering clinical practice, summarizing how machine learning, deep learning, and generative AI are being integrated into contemporary surgical workflows. Preoperative applications include detection of occult nodal metastasis and extranodal extension. Intraoperative innovations include augmented reality-assisted navigation, real-time margin assessment, and improving visual clarity and tissue handling for robotic platforms. Postoperatively, AI can predict complications like free flap failure and oncologic outcomes. Large language models are being operationalized for clinician-facing applications such as documentation and inbox support, as well as patient-facing education. Despite promising results, broad clinical deployment remains limited by concerns about privacy, validation, reliability, safety, and ethics. Widespread adoption will require prospective clinical trials, robust governance, and human-centered workflows that ensure AI remains a safe, assistive copilot. Full article

Background and Objectives: Postoperative pain after posterior spinal fusion (PSF) for adolescent idiopathic scoliosis (AIS) shows substantial interindividual variability, particularly during early mobilization. Although preoperative psychological vulnerability has been associated with less favorable pain trajectories in prior AIS research, evidence focused on the acute postoperative phase remains limited. This preliminary study evaluated whether preoperative psychological factors are associated with acute postoperative pain intensity, with separate assessment of resting and standing pain. Materials and Methods: A single-center retrospective cohort study included consecutive adolescents with AIS (<18 years) who underwent primary elective posterior instrumented spinal fusion between 1 January 2024 and 31 December 2025. Preoperative psychological variables were collected using validated instruments (STAIC-State, STAIC-Trait, Pain Catastrophizing Scale, HAQ/FDI, and inverted SRS-22). Pain intensity (VAS 0–10) was recorded at postoperative day (POD) 1, POD2, POD3, discharge, and 2-week follow-up in supine and standing positions. Derived endpoints included peak in-hospital standing pain, in-hospital standing pain burden (AUC), and standing–rest pain gaps. The prespecified inferential analysis used a linear mixed-effects model with fixed effects for time, position, preoperative STAIC-State, and position × STAIC-State interaction, with a patient-level random intercept. Results: Thirty-five patients were analyzed (mean age 15.2 ± 3.4 years; 62.9% female), with complete pain data at all timepoints. During hospitalization, standing pain was descriptively higher than resting pain (largest mean difference at POD2: 0.73 VAS points), with convergence at week 2 (both 1.52). In mixed-model analysis, pain significantly decreased at week 2 versus POD1 (β = −1.261, 95% CI −1.853 to −0.669; p < 0.001). Preoperative STAIC-State was not independently associated with postoperative pain (β = 0.030, 95% CI −0.065 to 0.124; p = 0.545), and no significant position × STAIC-State interaction was found (β = −0.008, 95% CI −0.079 to 0.064; p = 0.836). Conclusions: In this retrospective preliminary AIS cohort, postoperative pain improved significantly over time, while movement-evoked pain remained relevant during early recovery. In this preliminary cohort, no clear association was detected between preoperative state anxiety and acute postoperative pain intensity, supporting the need for broader multidimensional prognostic models in future prospective multicenter studies. Full article