Background: Cancer-related cognitive impairment (CRCI) is a frequent and distressing side effect among cancer survivors. While many patients report persistent cognitive difficulties, a notable discrepancy often exists between subjective complaints and objective performance on neuropsychological testing. This gap raises critical questions about self-awareness and metacognitive insight in the context of CRCI. Despite the clinical relevance of this phenomenon, conceptualized as anosognosia in other neurological conditions, its presence in oncology remains insufficiently explored.
Objective: This scoping review aims to map the existing literature on self-awareness of cognitive impairment in cancer survivors, with a focus on studies examining the discrepancy between subjective and objective cognition, the methodologies used to assess awareness, and the clinical and theoretical implications of impaired metacognition in this population.
Methods: A systematic search was conducted on PubMed for articles published between 2000 and 2025. Inclusion criteria comprised peer-reviewed studies involving adult cancer survivors that investigated subjective and/or objective cognitive functioning, and addressed aspects of self-awareness, metacognitive monitoring, or anosognosia. Studies were screened, selected, and charted following PRISMA-ScR guidelines.
Results: Forty six studies met the inclusion criteria. Most reported a weak or inconsistent correlation between self-reported and objectively measured cognition. A minority employed formal tools to assess metacognitive accuracy or insight. Methodological heterogeneity and a lack of consensus in terminology (e.g., "awareness," "insight," "complaints") limited cross-study comparisons. Only a small number of articles conceptualized this discrepancy in relation to anosognosia or broader models of self-awareness. Factors such as age, mood symptoms, fatigue, and neurobiological correlates (e.g., alterations in the default mode network) were identified as potential moderators of impaired awareness.
Conclusion: Despite growing evidence of subjective-objective cognitive discrepancies in cancer survivors, the construct of self-awareness remains under-theorized and inconsistently measured in the literature. There is an urgent need for standardization of terms and tools, and for theoretically informed approaches to capture metacognitive impairment in this context. Greater clarity in this domain may inform more tailored interventions, improve survivorship care, and advance the neuropsychological understanding of CRCI.
Background: People with multiple sclerosis (MS) often present with chronic comorbidities that complicate care and affect prognosis. Population-based evidence in Europe is limited.
Objective: To describe the prevalence of selected comorbidities among individuals with MS in Catalonia, Spain, using administrative health records.
Methods: We analysed data from 9,998 people diagnosed with MS (2013-2017), including demographic and diagnostic information across all care levels. Prevalence estimates for 23 comorbidities were stratified by age and sex, and, where available, compared with those of the general population.
Results: The MS population had a higher prevalence of psychiatric, metabolic, and autoimmune comorbidities in all age groups. Young women (18-40 years) showed increased rates of depression, anxiety, thyroid disease, and migraine, while middle-aged men (50-60 years) had higher rates of hypertension, hyperlipidaemia, and diabetes.
Conclusion: MS is associated with a substantial, early-onset comorbidity burden, underscoring the need for integrated, multidisciplinary care across the life course.
Background: To identify clinical factors associated with pain perception, psychological status, and sleep quality in patients with trigeminal neuralgia (TN) and to evaluate the clinical efficacy of individualized comprehensive nursing interventions in alleviating pain, anxiety, depression, and sleep disorders.
Methods: This study combined retrospective with prospective design. The retrospective analysis included 162 patients with TN admitted to our hospital from January 2020 and March 2022. Patients were grouped based on their scores on the Visual Analogue Scale (VAS), Hospital Anxiety and Depression Scale (HADS), and Pittsburgh Sleep Quality Index (PSQI). In the prospective arm, 64 eligible patients were assigned into intervention (individualized nursing care) and control (standard routine care) groups using a random number table. Changes in VAS, HADS, and PSQI scores before and after the intervention were compared, along with assessments of nursing adherence and patient satisfaction.
Results: Retrospective analysis revealed that sex, frequency of pain episodes, comorbid hypertension, and history of surgical treatment were associated with pain scores. Pain distribution and previous treatment methods were correlated with anxiety/depression. Frequency of pain and pain distribution were major factors affecting sleep quality. In the prospective study, the intervention group showed significantly lower VAS, HADS-Anxiety, HADS-Depression, and PSQI scores. The intervention group also demonstrated better adherence to nursing and higher nursing satisfaction.
Conclusion: Risk-factor based targeted nursing interventions can alleviate pain, negative emotions, and sleep disturbances, while enhancing adherence and patient satisfaction, demonstrating strong clinical value in TN management.
Background: Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by motor and non-motor symptoms. During Ramadan, fasting Muslims abstain from food, drink, and often medications between sunrise and sunset.
Objective: To review the clinical considerations, therapeutic strategies, and cultural factors relevant to managing PD patients during Ramadan fasting, and to provide practical recommendations for neurologists and healthcare providers.
Methods: This review synthesized existing guidelines (e.g., BIMA Ramadan Compendium), literature on drug pharmacokinetics during fasting, and clinical expertise from PD specialists in Middle Eastern and global Muslim populations. Discussions at a PD consensus meeting informed a stepwise algorithm for individualized care.
Results: Pre-Ramadan risk assessment is essential, with stratification by disease stage. Early PD (Hoehn and Yahr stage 1-2) patients on monotherapy may fast safely with minimal adjustments, while moderate PD (Hoehn and Yahr stage 3) with multiple daily levodopa doses or combination therapy, requires consolidation of levodopa doses, addition of long-acting agents, and avoidance of dose stacking. Advanced PD patients who have troublesome motor/non-motor fluctuations and dyskinesias as well, and are taking medications multiple times per day are often unsuitable for fasting. Common complications include response fluctuations, dyskinesias, and sleep disturbances exacerbated by altered circadian rhythms. Long-acting dopaminergic therapies, including Dopamine Agonists (rotigotine patches and other extended-release (ER) oral agents), adjunctive agents (opicapone, rasagilline and safinamide), and Device-Aided Treatments (DAT; subcutaneous foslevodopa-foscarbidopa, subcutaneous continous subcutaneous apomorphine infusion, levodopa-carbidopa intestinal gel and deep brain stimulation) can help stabilize motor and non-motor fluctuations. Sleep hygiene measures and behavioral adjustments further support patient well-being. Cultural and spiritual motivations strongly influence adherence, requiring sensitive counseling and involvement of caregivers and religious leaders.
Conclusion: Safe Ramadan fasting in PD requires comprehensive pre-Ramadan assessment, stage-specific therapeutic strategies, and proactive management of both motor and non-motor complications. Shared decision-making that integrates medical, psychological, and religious considerations is vital to optimize patient outcomes while respecting spiritual values.
Introduction: Myasthenia gravis (MG) is a rare autoimmune disease characterized by skeletal muscle weakness. As limited real-world data are available in Japan, we aimed to describe the humanistic burden of disease (primary aim), mainly with regards to health-related quality of life (HRQoL), and treatment patterns (secondary aim) in patients with generalized MG (gMG).
Methods: Data were drawn from the Adelphi Real World MG Disease Specific Programme™, a cross-sectional survey of neurologists and their patients in Japan from August 2023 and January 2024. Analyses were descriptive.
Results: Overall, 40 neurologists reported data for 128 patients, where 29 patients had self-reported data. Mean (standard deviation) patient age was 57.9 (16.0) years and 53.9% were female. At data collection, 98.4% of patients were receiving maintenance therapy (including novel treatments). Nonsteroidal immunosuppressant therapies were used at first-line of therapy in 54.5% of cases (n = 67/123). Oral systemic steroids were most used [78.0% of patients at first-line (n = 96/123), 77.9% at second-line (n = 53/68), and 75.0% at third-line (n = 15/20)]. The median (IQR) duration from symptom onset to diagnosis was 2.0 (0.9-4.3) months. Of 28 patients with EQ-5D-5L data, 46.4% reported difficulties with usual activities, 42.9% with mobility, 21.4% with self-care, 53.6% with pain/discomfort, and 39.3% with anxiety/depression.
Conclusion: Most patients in this Japanese cohort with gMG received maintenance therapy and the time from symptom onset to diagnosis was relatively short. However, impaired HRQoL remained.
Background: Delayed cerebral ischemia (DCI) remains a leading cause of secondary neurological deterioration and mortality after aneurysmal subarachnoid hemorrhage (aSAH). Accumulating evidence highlights the pivotal role of systemic inflammation in the pathogenesis of DCI, with peripheral inflammatory markers showing potential as early indicators. However, the predictive performance of individual biomarkers is limited. By leveraging machine learning (ML) techniques, it is possible to integrate heterogeneous inflammatory signals and model complex nonlinear relationships to improve individualized risk prediction.
Methods and materials: We conducted a retrospective analysis of 562 aSAH patients admitted to a single tertiary center. Clinical, radiographic, and laboratory data-including peripheral inflammatory indices-were extracted from electronic medical records. The Boruta algorithm was applied for feature selection. Six ML models were developed and compared: logistic regression, neural network, random forest, support vector machine, gradient boosting machine (GBM), and extreme gradient boosting (XGBoost). Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, F1 score, calibration curves, and decision curve analysis (DCA).
Results: Among the six models, the neural network demonstrated the best balance between discrimination and calibration, with an AUC of 0.826 in the training cohort and 0.808 in the internal testing cohort. Eight predictors were included in the final model: Glasgow Coma Scale (GCS), Hunt-Hess grade, modified Fisher score, prognostic nutritional index (PNI), neutrophil-to-albumin ratio (NAR), neutrophil-to-lymphocyte platelet ratio (NLPR), C-reactive protein-to-lymphocyte ratio (CLR), and procalcitonin. SHapley Additive exPlanations (SHAP) analysis revealed Hunt-Hess grade and procalcitonin as top contributors.
Conclusion: This study proposes a machine learning-based risk prediction tool for DCI after aSAH, built from routinely available inflammatory and clinical variables. The model demonstrated strong discriminative and calibration performance and provides a clinically interpretable, preoperative decision-support tool. Prospective multicenter validation is warranted to assess generalizability and facilitate clinical translation.
Objective: To explore the feasibility and clinical value of constructing a therapeutic efficacy prediction model for patients with lower limb motor dysfunction after stroke who received conventional treatment combined with functional electrical stimulation (FES) mirror therapy training, based on age, baseline National Institutes of Health Stroke Scale (NIHSS) score, baseline FMA score, FES stimulation intensity, FES stimulation frequency, and mirror therapy training duration.
Methods: A total of 510 patients with lower limb motor dysfunction after stroke admitted to the hospital from January 2022 to October 2024 were selected and divided into a training set (n = 357) and a validation set (n = 153) at a ratio of 7:3. The clinical data of the patients were collected, and the FES stimulation parameters and mirror therapy training data were recorded. The modified Fugl-Meyer Motor Assessment Scale (FMA) was used to evaluate the therapeutic efficacy (effective was defined as an improvement of FMA score ≥ 15 points). Independent risk factors were screened by univariate and multivariate Logistic regression, a Nomogram model was constructed, and its efficacy was evaluated and verified.
Results: The effective treatment rate was 65.83% (235/357) in the training set and 64.05% (98/153) in the validation set. Multivariate regression showed that age, baseline NIHSS score, baseline FMA score, FES stimulation intensity, FES stimulation frequency, and mirror therapy training duration were independent influencing factors (All p < 0.05). The C-indices of the Nomogram model in the training set and the validation set were 0.792 and 0.778 respectively, and the AUCs were 0.789 (95% CI: 0.728-0.851) and 0.774 (95% CI: 0.681-0.867) respectively. The sensitivities and specificities were 0.779, 0.700 and 0.714, 0.738, respectively. The calibration curves showed good consistency between the predicted values and the actual values, and the P-values of the Hosmer-Lemeshow test were 0.866 and 0.442, respectively.
Conclusion: The Nomogram model constructed based on the above indicators can effectively predict the therapeutic efficacy of patients with lower limb motor dysfunction after stroke, providing a basis for clinical individualized intervention.
Background: The incidence of postoperative complications following cranioplasty (CP) procedures remains relatively high, which has a significant impact on patient prognosis. While current research on predictive factors for complications has focused primarily on patient demographics, the timing of surgery and material selection, the association between skin flap shift and complications has yet to be systematically evaluated.
Objective: To investigate the correlation between skin flap shift and postoperative complications following CP.
Methods: A cohort of patients undergoing CP was enrolled and categorized into postoperative complication and no-complication groups. First, we conducted a univariate analysis on the following variables: age; gender; medical history; and surgical variables. Variables with a p-value of ≤0.2 in the univariate analysis were included in the multivariate logistic regression analysis. For the continuous variables, ROC curves were used to determine the optimal cut-off values for predicting complications. These values were then converted into binary variables for the multivariate analysis.
Results: Univariate analysis demonstrated that the differences in the materials utilized for repair, intraoperative blood loss, and skin flap shift between the two groups were statistically significant. The optimal cutoff values for intraoperative blood loss and skin flap shift, as determined by ROC curve analysis, were identified as 175 mL and 13.55 mm, respectively. Multivariate logistic regression analysis identified skin flap shift to be independently associated with postoperative complications after CP. (OR: 3.239, 95% CI: [1.450-7.237], p = 0.004). The area under the curve for predicting postoperative complications based on skin flap shift was 0.719 (95%CI: 0.646-0.797).
Conclusion: Skin flap shift was independently associated with postoperative complications following CP surgery. Patients with flap displacements exceeding 13.55 mm are at an increased risk of experiencing such complications.
This study aimed to characterize the morphology of the cerebral compliance monitoring curve in patients with primary headaches, specifically differentiating between migraine with aura and migraine without aura, using non-invasive intracranial monitoring. This study is innovative in that it applies Brain4Care® technology in an outpatient setting to differentiate migraine types, making a significant contribution to understanding the pathophysiology of headaches and thus improving clinical management strategies. A cross-sectional, prospective study was carried out with 50 patients seen at an outpatient clinic specializing in pain. Variables such as the P2/P1 ratio, time to peak (TTP), and the morphology of the cerebral compliance wave were assessed, as well as demographic and clinical factors. A high prevalence of altered P2/P1 ratio (P2 > P1) was observed in patients suffering from migraine with aura. Statistical analyses indicated significant associations between this ratio and factors such as age and the presence of symptoms at the time of the examination. The findings emphasize the importance of the P2/P1 ratio and TTP as indicators for differentiating primary headaches. A non-invasive intracranial monitoring offers valuable insights into brain dynamics, enabling more accurate diagnoses and personalized interventions in outpatient settings. Brain4Care® technology is emerging as a promising tool for the non-invasive monitoring of cerebral compliance, with the potential to revolutionize clinical management of migraines. Future studies should extend the validation of these findings and explore new applications for this technology in clinical practices.
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