Background: The most effective strategy for managing cancer pain remotely should be better defined. There is a need to identify those patients who require increased attention and calibrated follow-up programs. Methods: Machine learning (ML) models were developed using the data prospectively obtained from a single-center program of telemedicine-based cancer pain management. These models included random forest (RF), gradient boosting machine (GBM), artificial neural network (ANN), and the LASSO–RIDGE algorithm. Thirteen demographic, social, clinical, and therapeutic variables were adopted to define the conditions that can affect the number of teleconsultations. After ML validation, the risk analysis for more than one remote consultation was assessed in target individuals. Results: The data from 158 patients were collected. In the training set, the accuracy was about 95% and 98% for ANN and RF, respectively. Nevertheless, the best accuracy on the test set was obtained with RF (70%). The ML-based simulations showed that young age (<55 years), lung cancer, and occurrence of breakthrough cancer pain help to predict the number of remote consultations. Elderly patients (>75 years) with bone metastases may require more telemedicine-based clinical evaluations. Conclusion: ML-based analyses may enable clinicians to identify the best model for predicting the need for more remote consultations. It could be useful for calibrating care interventions and resource allocation.

No abstract

Pain and nociception are different phenomena. Nociception is the result of complex activity in sensory pathways. On the other hand, pain is the effect of interactions between nociceptive processes, and cognition, emotions, as well as the social context of the individual. Alterations in the nociceptive route can have different genesis and affect the entire sensorial process. Genetic problems in nociception, clinically characterized by reduced or absent pain sensitivity, compose an important chapter within pain medicine. This chapter encompasses a wide range of very rare diseases. Several genes have been identified. These genes encode the Nav channels 1.7 and 1.9 (SCN9A, and SCN11A genes, respectively), NGFβ and its receptor tyrosine receptor kinase A, as well as the transcription factor PRDM12, and autophagy controllers (TECPR2). Monogenic disorders provoke hereditary sensory and autonomic neuropathies. Their clinical pictures are extremely variable, and a precise classification has yet to be established. Additionally, pain insensitivity is described in diverse numerical and structural chromosomal abnormalities, such as Angelman syndrome, Prader Willy syndrome, Chromosome 15q duplication syndrome, and Chromosome 4 interstitial deletion. Studying these conditions could be a practical strategy to better understand the mechanisms of nociception and investigate potential therapeutic targets against pain.

Background: Juvenile primary fibromyalgia syndrome (JPFS) is a chronic musculoskeletal pain syndrome that affects children and adolescents. Methods: A VOSviewer-based bibliometric network analysis was performed by scanning the global literature on JPFS in the Web of Science (WOS) online database. The search string applied to identify the closest matching articles was “juvenile primary fibromyalgia syndrome (all field)”. Results: A total of 67 articles on JPFS were published from 1985 to March 2022, in the WOS. Regarding article types, 39 were research manuscripts, 16 reviews, 8 meeting abstracts, 2 letters, 1 book chapter, 1 correction, and 1 proceeding paper. The Quartile analysis demonstrated that 44% of papers were published in Q1, 37% in Q2, 8% in Q3, and 11% in Q4. Conclusions: Our analysis highlights that more efforts are warranted to increase the production of quality papers and enhance the connections between the various research groups. JFPS represents a research field still to be explored and which deserves greater investments to obtain quality scientific evidence.

Background: Percutaneous electrical nerve stimulation (PENS) is a minimally invasive peripheral neuromodulation approach implemented against chronic neuropathic and mixed pain. This bibliometric study aims to quantitatively evaluate the output of PENS for pain treatment in the scientific literature. The main purpose is to stimulate research in the field and bridge potential scientific gaps. Methods: Articles were retrieved from the Web of Science (WOS) database. The search key term was “percutaneous electrical nerve stimulation (All Fields) and pain (All Fields)”. Year of publication, journal metrics (impact factor and quartile, Q), title, document type, topic, and citations were extracted. The join-point regression was implemented to assess differences in time points for the publication output. The software tool VOSviewer (version 1.6.17) was used for the visual analysis. Results: One thousand three hundred and eighteen articles were included in the knowledge visualization process. A linear upward trend for annual new publications was found. Almost two-thirds of the documents were published in top-ranked journals (Q1 and Q2). The topic “efficacy” was prevalent (12.81%). Concerning article type, the search strategy yielded 307 clinical investigations (23.3%). Articles were cited 36,610 times with a mean of 42.4 citations per article. Approximately one-half of the articles were cited less than 23 times in a range of 21 years. The semantic network analysis for keywords found eight clusters. The analysis of collaborative efforts among researchers showed five thematic clusters including 102 authors with a minimum of five documents produced in collaborations. Most partnerships involved the United States, England, and Germany. Conclusions: despite the upward trend in the number of publications on the subject and the publication of articles in top-ranked journals, there is a need to increase scientific collaborations between researchers and institutions from different countries.