Introduction: Central post-stroke pain (CPSP) is defined as the neuropathic pain that arises either acutely or in the chronic phase of a cerebrovascular event and is a result of central lesions of the somatosensory tract. The aim of this systematic review and meta-analysis was to establish the prevalence of CPSP, to describe its characteristics, and to discuss the associated management challenges. Methods: After a systematic Medline search, we identified 69 papers eligible to be included. Results: The pooled prevalence of CPSP in patients with stroke at any location was 11% (95% CI 7-18%), which can increase to more than 50% in the subgroups of patients with medullary or thalamic strokes. CPSP onset coincides with stroke occurrence in 26% of patients (95% CI 18-35%); CPSP manifests within a month since symptom onset in 31% of patients (95% CI 22-42%), and occurs between the first month and the first year in 41% of patients (95% CI 33.9-49.0%). CPSP develops more than 12 months after stroke onset in 5% of patients (95% CI 3-8%). Conclusions: Clinicians should look for any evidence of central neuropathic pain for at least 12 months after stroke. Both pharmacological and non-pharmacological interventions can be used for the management of CPSP. Lamotrigine has the strongest evidence (Level II of evidence, derived from small randomized controlled trials) for being effective in the management of CPSP. Future research should focus on well-designed trials of pharmacological and non-pharmacological interventions aiming to relief CPSP, which is a very common but often neglected pain syndrome.

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Introduction: Peripheral neuropathic pain (PNP) arises either acutely or in the chronic phase of a lesion or disease of the peripheral nervous system and is associated with a notable disease burden. The management of PNP is often challenging. The aim of this systematic review was to evaluate current evidence, derived from randomized controlled trials (RCTs) that have assessed pharmacological interventions for the treatment of PNP due to polyneuropathy (PN). Methods: A systematic search of the PubMed database led to the identification of 538 papers, of which 457 were excluded due to not meeting the eligibility criteria, and two articles were identified through screening of the reference lists of the 81 eligible studies. Ultimately, 83 papers were included in this systematic review. Results: The best available evidence for the management of painful diabetic polyneuropathy (DPN) is for amitriptyline, duloxetine, gabapentin, pregabalin and venlafaxine as monotherapies and oxycodone as add-on therapy (level II of evidence). Tramadol appears to be effective when used as a monotherapy and add-on therapy in patients with PN of various etiologies (level II of evidence). Weaker evidence (level III) is available on the effectiveness of several other agents discussed in this review for the management of PNP due to PN. Discussion: Response to treatment may be affected by the underlying pathophysiological mechanisms that are involved in the pathogenesis of the PN and, therefore, it is very important to thoroughly investigate patients presenting with PNP to determine the causes of this neuropathy. Future RCTs should be conducted to shed more light on the use of pharmacological approaches in patients with other forms of PNP and to design specific treatment algorithms.

Introduction: Pain is the unpleasant sensation and emotional experience that leads to poor quality of life for millions of people worldwide. Considering the complexity in understanding the principles of pain and its significant impact on individuals and society, research focuses to deliver innovative pain relief methods and techniques. This review explores the clinical uses of machine learning (ML) for the diagnosis, classification, and management of pain. Methods: A systematic review of the current literature was conducted using the PubMed database library.Results: Twenty-six papers related to pain and ML research were included. Most of the studies used ML for effectively classifying the patients' level of pain, followed by use of ML for the prediction of manifestation of pain and for pain management. A less common reason for performing ML analysis was for the diagnosis of pain. The different approaches are thoroughly discussed. Conclusion: ML is increasingly used in pain medicine and appears to be more effective compared to traditional statistical approaches in the diagnosis, classification, and management of pain.

Introduction: Peripheral neuropathic pain (PNP) is defined as the neuropathic pain that arises either acutely or in the chronic phase of a lesion or disease affecting the peripheral nervous system. PNP is associated with a remarkable disease burden, and there is an increasing demand for new therapies to be used in isolation or combination with currently available