Percutaneous discectomy under CT and fluoroscopy guidance: an international multicentric study.

PURPOSE: To evaluate the technical efficacy, safety, and reproducibility of automated percutaneous lumbar discectomy (APLD) under CT and fluoroscopic guidance, for treating radiculopathy caused by lumbar disc herniation in patients impervious to conservative treatment. METHODS: A total of 77 patients with symptomatic lumbar disc herniation were treated with APLD in a prospective multicentric study performed in four centers across three countries. Magnetic resonance imaging and/or computed tomography was used to evaluate the disc herniation before and after the procedure. Only local anesthesia was used during these procedures. Clinical outcomes were measured with the visual analog scale (VAS) for pain at one and 6 months after the procedure. RESULTS: Technical success rate was 100% with a mean intervention duration of 30 min (15-45 min). No complications occurred during the procedure. Post-lumbar puncture syndrome occurred in three patients who were successfully treated with blood patches. VAS decreased from a mean of 8 before the intervention to 3 1 month after (p value = 0.001). The requirement for analgesia decreased from 100 to 27%. No statistically significant differences in outcomes were found between the centers. CONCLUSION: APLD with dual imaging guidance under local anesthesia is a safe, feasible, and reproducible technique to treat symptomatic lumbar disc herniation.

Using a nonparametric item response theory model to identify patterns of cognitive decline: The Mokken scale analysis.

Cognitive decline, particularly in dementia, presents complex challenges in early detection and diagnosis. While Item Response Theory (IRT) has been instrumental in identifying patterns of cognitive impairment through psychometric tests, its parametric models often require large sample sizes and strict assumptions. This creates a need for more adaptable, less demanding analytical methods. This study aimed to evaluate the effectiveness of Mokken scale analysis (MSA), a nonparametric IRT model, in identifying hierarchical patterns of cognitive impairment from psychometric tests. Using data from 1164 adults over 60 years old, we applied MSA to the orientation subscale of ACE-III. Our analysis involved calculating scalability, monotone homogeneity, invariant item ordering (IIO) and response functions. The MSA effectively retrieved the hierarchical order of cognitive impairment patterns. Most items showed strong scalability and consistent patterns of cognitive performance. However, challenges with IIO were observed, particularly with items having adjacent difficulty parameters. The findings highlight MSA's potential as a practical alternative to parametric IRT models in cognitive impairment research. Its ability to provide valuable insights into patterns of cognitive deterioration, coupled with less stringent requirements, makes it a useful tool for clinicians and researchers.

Application of machine learning techniques for dementia severity prediction from psychometric tests in the elderly population.

Previous research has shown the benefits of early detection and treatment of dementia. This detection is usually performed manually by one or more clinicians based on reports and psychometric testing. Machine learning algorithms provide an alternative method of prediction that may contribute, with an automated process and insights, to the diagnosis and classification of the severity level of dementia. The aim of this study is to explore the use of neuropsychological data from a reduced version of the Addenbrooke's Cognitive Examination III (ACE-III) to predict absence or different levels of dementia severity using the Global Deterioration Scale (GDS) scores through the implementation of the kNN machine learning algorithm. A sample of 1164 elderly people over sixty years old were evaluated using a reduced version of the ACE-III and the GDS. The kNN classifier provided good accuracies using 15 items from the ACE-III and adequately differentiating people with absence and mild impairment, from those with more severe levels of impairment according to the GDS rating. Our results suggest that the kNN algorithm may be used to automate aspects of clinical cognitive impairment classification in the elderly population.

The cumulative effect of fibromyalgia symptoms on cognitive performance: The mediating role of pain.

Fibromyalgia (FMS) is a chronic condition that encompasses widespread pain associated with cognitive impairment and significant emotional distress related to functional disability. This study aimed to obtain evidence of the role of pain in the effect of time since FMS diagnosis and cognitive performance using a novel online protocol of neuropsychological evaluation since the COVID-19 pandemic has challenged traditional neuropsychology testing leading to the need for novel procedures transitioning to tele-neuropsychology. A sample of 70 adult women was evaluated (50 with FMS and 19 controls) using online questionnaires that evaluated pain and executive functioning (impulsivity, inhibition control, monitoring, and planning). Afterward, participants were evaluated by trained neuropsychologists in a 30 min online session using virtually adapted cognitive tests: the Hopkins Verbal Learning Test (memory), the Symbol-Digit Modalities Test (attention and speed processing), the F-A-S test (verbal fluency), and Digit Span tests (working memory). We found that the time of FMS diagnosis has an effect on cognitive functioning predominantly mediated by pain. Our results point out the role of pain as a mediator on cognitive performance, specifically in executive functions which are directly affected by the cumulative effect of the time of diagnosis. Furthermore, the importance of considering a broader perspective for assessment and treatment including novel procedures via tele-neuropsychology.