Unlike traditional video games developed solely for entertainment purposes, game-based learning employs intentionally crafted approaches that seamlessly merge entertainment and educational content, resulting in captivating and effective learning encounters. These pedagogical methods include serious video games and gamification. Serious games are video games utilized as tools for acquiring crucial (serious) knowledge and skills. On the other hand, gamification requires integrating gaming elements (game mechanics) such as points, leaderboards, missions, levels, rewards, and more, into a context that may not be associated with video gaming activities. They can be dynamically (game dynamics) combined developing various strategic approaches. Operatively, gamification adopts simulation elements and leverages the interactive nature of gaming to teach players specific skills, convey knowledge, or address real-world issues. External incentives stimulate internal motivation. Therefore, these techniques place the learners in the central role, allowing them to actively construct knowledge through firsthand experiences.Anesthesia, pain medicine, and critical care demand a delicate interplay of technical competence and non-technical proficiencies. Gamification techniques can offer advantages to both domains. Game-based modalities provide a dynamic, interactive, and highly effective opportunity to learn, practice, and improve both technical and non-technical skills, enriching the overall proficiency of anesthesia professionals. These properties are crucial in a discipline where personal skills, human factors, and the influence of stressors significantly impact daily work activities. Furthermore, gamification can also be embraced for patient education to enhance comfort and compliance, particularly within pediatric settings (game-based distraction), and in pain medicine through stress management techniques. On these bases, the creation of effective gamification tools for anesthesiologists can present a formidable opportunity for users and developers.This narrative review comprehensively examines the intricate aspects of gamification and its potentially transformative influence on the fields of anesthesiology. It delves into theoretical frameworks, potential advantages in education and training, integration with artificial intelligence systems and immersive techniques, and also addresses the challenges that could arise within these contexts.

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 (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.

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.

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.

Introduction: Effective pain management is crucial for patient care, impacting comfort, recovery, and overall well-being. Traditional subjective pain assessment methods can be challenging, particularly in specific patient populations. This research explores an alternative approach using computer vision (CV) to detect pain through facial expressions. Methods: The study implements the YOLOv8 real-time object detection model to analyze facial expressions indicative of pain. Given four pain datasets, a dataset of pain-expressing faces was compiled, and each image was carefully labeled based on the presence of pain-associated Action Units (AUs). The labeling distinguished between two classes: pain and no pain. The pain category included specific AUs (AU4, AU6, AU7, AU9, AU10, and AU43) following the Prkachin and Solomon Pain Intensity (PSPI) scoring method. Images showing these AUs with a PSPI score above 2 were labeled as expressing pain. The manual labeling process utilized an open-source tool, makesense.ai, to ensure precise annotation. The dataset was then split into training and testing subsets, each containing a mix of pain and no-pain images. The YOLOv8 model underwent iterative training over 10 epochs. The model’s performance was validated using precision, recall, and mean Average Precision (mAP) metrics, and F1 score. Results: When considering all classes collectively, our model attained a mAP of 0.893 at a threshold of 0.5. The precision for “pain” and “nopain” detection was 0.868 and 0.919, respectively. F1 scores for the classes “pain”, “nopain”, and “all classes” reached a peak value of 0.80. Finally, the model was tested on the Delaware dataset and in a real-world scenario. Discussion: Despite limitations, this study highlights the promise of using real-time computer vision models for pain detection, with potential applications in clinical settings. Future research will focus on evaluating the model’s generalizability across diverse clinical scenarios and its integration into clinical workflows to improve patient care.