Head and neck cancers (HNCs) represent a heterogeneous group of malignancies with high morbidity and complex functional impairment. Despite advances in multimodal therapy, many patients present with advanced or incurable disease, demanding early integration of palliative care. To review the epidemiology, symptom burden, and best practices in palliative care for patients with advanced HNC, highlighting strategies for symptom management, communication, and end-of-life decision-making. A narrative review of current literature, clinical guidelines (National Comprehensive Cancer Network (NCCN), American Society of Clinical Oncology (ASCO), UK National Multidisciplinary Guidelines), and key studies on palliative interventions in HNC was conducted. Emphasis was placed on early integration of palliative care, multidisciplinary management, and evidence-based approaches to symptom control. HNC patients experience high symptom burden, including pain, dysphagia, airway compromise, hemorrhage, and psychological distress. Early palliative care integration improves symptom control, quality of life, and alignment of treatment with patient goals. Multidisciplinary approaches involving surgeons, oncologists, palliative specialists, speech therapists, dietitians, and psychosocial support are essential. Sentinel clinical events should trigger structured discussions on prognosis, treatment objectives, and end-of-life preferences. Advance care planning, including documentation of "do not resuscitate" (DNR) orders and preferred location of death, is critical in the terminal phase. Effective palliative care in HNC requires early, proactive, and multidisciplinary strategies that balance disease-directed therapy with patient-centered goals. Standardization of care pathways and ongoing evaluation of emerging evidence are essential to optimize symptom management, facilitate informed decision-making, and enhance quality of life for patients with advanced diseases.

Artificial intelligence (AI) chatbots powered by large language models (LLMs) show promise in human-like responses, yet their utility for complex biomedical issues remains uncertain. We evaluated four leading LLMs, including ChatGPT-5.2, Claude 4.5 Sonnet, Gemini 3.0 Pro, and DeepSeek-R1, on their performance in addressing queries related to bone metastasis (BM) and cancer-induced bone pain (CIBP). Using 18 structured questions derived from bibliometric analysis, we assessed responses across research, clinical, and patient perspectives. Qualitatively rating based on accuracy, readability, completeness, conciseness, and empathy demonstrated that LLMs efficiently provide intuitive information regarding BM and CIBP. However, due to limitations such as data bias, weak interpretability, and ambiguous accountability, these tools should be regarded as supportive assistants under human experts' supervision. Although LLMs hold substantial promise for the medical field, it is essential to optimize model architecture and develop comprehensive evaluation standards for specialized domains.

Poorly aligned driving posture in the occupational environment increases the risk of musculoskeletal disorders. Asymmetric pressure distribution on the neck and shoulder areas during driving can lead to severe pain and soreness. Although occupational drivers are extremely focused on safe driving, they are less aware of their seating posture. Although several automated systems have been developed for monitoring driver behavior and detecting driver drowsiness, an alert system with the capability of recognizing the driver posture can assist drivers in maintaining an optimal seating posture and minimizing ergonomic risks. To develop an efficient machine learning-based driver posture recognition system, a dataset that efficiently represents improper seating postures is required. To fulfill this requirement, this paper proposes the DriveNet dataset, a comprehensive dataset developed for the purpose of driver posture recognition. This dataset comprises 11,160 high-resolution RGB images of four posture classes, which are captured using a high-resolution RGB camera installed on the Android smartphone camera. This dataset is intended to facilitate further research on driver posture classification and recognition using advanced machine learning and deep learning techniques.

Polyneuritis cranialis is characterized by the simultaneous or sequential inflammation of multiple cranial nerves, which may occur unilaterally or bilaterally. Although it is often related to infection, its exact etiology remains unclear. Due to its nonspecific clinical manifestations, diagnosis typically relies on the exclusion of other conditions. Herein, we report a case of postinfectious polyneuritis cranialis. The patient presented to our hospital with restricted mouth opening, dysphagia, coughing while drinking, dysarthria, and posterior neck pain following a finger injury. Laboratory tests showed markedly elevated inflammatory markers. Neurological examination revealed involvement of cranial nerves V, IX, X, and XII. Motor nerve conduction studies of the facial nerve suggested partial facial nerve damage. Brain magnetic resonance imaging demonstrated mild nonspecific white matter changes. After exclusion of alternative diagnoses, the patient was diagnosed with polyneuritis cranialis. The patient's condition improved following corticosteroid pulse therapy and was subsequently discharged. This case highlights that the diagnosis of polyneuritis cranialis remains one of exclusion and is often clinically challenging. When encountering patients with rapidly progressive cranial nerve palsies, polyneuritis cranialis should be included in the differential diagnosis after more common structural or systemic etiologies have been excluded.

BACKGROUND Osteoporotic femoral neck fractures cause substantial disability in older adults. Although joint replacement is the standard treatment, the choice between total hip arthroplasty (THA) and hemiarthroplasty (HA) remains controversial. This study compared outcomes of THA and HA and identified factors influencing postoperative hip functional recovery. MATERIAL AND METHODS This retrospective study included 98 patients, with 52 in the THA group and 46 in the HA group. Surgical parameters and outcomes were compared, including hip function assessed by the Harris Hip Score (HHS), pain evaluated using the visual analog scale (VAS), and physical performance measured by the Short Physical Performance Battery (SPPB) at 3, 6, and 12 months postoperatively. Postoperative complications were recorded. Good functional recovery was defined as HHS ≥80 at 1 year. Univariate and multivariate logistic regression analyses were performed to identify factors associated with functional recovery. RESULTS The THA group had significantly longer operative time, greater intraoperative blood loss, and higher postoperative drainage volume than the HA group (P<0.01). At 6 and 12 months, THA patients showed significantly better hip function, physical performance, and lower activity-related VAS scores (P<0.05). Overall complication rates were similar (P>0.05); however, acetabular wear-related pain occurred in 4 patients only in the HA group. Multivariate analysis showed that younger age, higher bone mineral density T-score, THA, and good rehabilitation compliance independently predicted favorable functional recovery (P<0.05). CONCLUSIONS THA provides superior medium- to long-term functional outcomes compared with HA. Surgical decisions should consider age, bone quality, functional demands, and rehabilitation compliance, prioritizing THA when appropriate.