Background/Objectives: Electrical threshold testing (ETT) offers a promising method for assessing somatosensory function. Despite its growing use, fundamental aspects such as the physiological recovery time required between repeated threshold measurements remain poorly understood. This gap is critical when evaluating sensory, motor, or pain thresholds (EST, EMT, EPT) in pre–post designs or rapid intra-session protocols. The aim is to investigate the short-term recovery dynamics of electrical thresholds following electrical threshold testing, and to determine the minimum interval required for values to return to a stable baseline. Methods: In this pilot, repeated-measures study, 10 healthy adults (20 upper limbs) underwent three progressive stimulation trials (sensory, motor, and pain). Electrical thresholds were assessed at fixed recovery intervals (0–120 s), with duplicate measurements at each time point. Stability was defined as the absence of significant differences between repeated measures. Results: EST stabilized rapidly after sensory or motor stimulation, showing no significant differences beyond 0 and 15 s, respectively. Within pain stimulation, EST recovered at 60 s. EMT showed immediate recovery with motor stimulation and required longer recovery with pain stimulation, with stabilization observed at 90 s. EPT exhibited the highest variability, with the smallest time-dependent differences observed immediately after the first assessment. Conclusion: Recovery time after electrical stimulation varies by threshold type and intensity of the stimuli. EST and EMT can be reliably reassessed immediately after sensory and motor stimulation, respectively. However, when stimulation reaches EPT level, EST requires 60 s to recover and EMT needs 90 s. EPT demonstrates higher variability, indicating the need for further investigation. These findings support the implementation of standardized recovery intervals in ETT and underscore the importance of interpreting EPT results with caution during rapid assessments.

Introducción: El desarrollo normativo de la Ley 44/2003, a través del Real Decreto 639/2014, inició el proceso de reorganización de la Formación Sanitaria Especializada (FSE). El objetivo de este trabajo es elaborar una propuesta de competencias específicas para la especialidad de Medicina Preventiva y Salud Pública mediante un análisis comparado y proceso participativo. Métodos: Cuatro fases: 1) análisis y extracción de competencias de documentación de organismos oficiales; 2) consulta dirigida a personas clave; 3) consulta abierta a residentes y personas implicadas en la FSE, y 4) difusión a la Comisión Nacional de la Especialidad y público general. Resultados: 1) Se extrajeron 543 competencias y 67 categorías de 7 fuentes primarias (Austria, Canadá, ECDC, Estados Unidos, Francia, Reino Unido y OPS). Se produjeron 126 competencias en 12 categorías. 2) Participaron 10 personas clave, 64 competencias fueron modificadas, 10 eliminadas y 9 nuevas. 3) Hubo 32 respuestas: 132 competencias en 12 categorías. Propuesta final: 145 competencias en 21 categorías, organizadas en 3 bloques: competencias genéricas, técnicas y específicas. Conclusión: La propuesta final es producto de la participación de residentes y personas implicadas en la FSE, partiendo del actual marco y del análisis del desarrollo de la especialidad en el contexto internacional. Se han incorporado conceptos presentes en países de nuestro entorno y cercanos a la práctica. Introduction: The Royal Decree 639/2014 (‘Core Curriculum’ Decree) has amongst its objectives to modify Specialist Training in Medicine Disciplines. The aim of this project is to elaborate a proposal of specific skills for the specialty of Preventive Medicine and Public Health using a comparative and participative approach. Methods: 1) Comparative analysis of documents published by official institutions; 2) consultation with key informants; 3) open consultation with residents and trainers, and 4) presentation to the National Commission of the Specialty and the general public. Results: 1) 126 competencies were found in 12 categories. 2) 10 key informants, 64 skills modified, 10 removed, and 9 added; 3) 32 responses the first draft contained 132 skills in 12 categories. The final proposal included 145 skills in 21 categories, classified into 3 areas: generic, technical, and specific skills. Conclusion: The final proposal is the product of participation of residents and individuals involved in specialised training, starting from the current framework and international context analysis. Concepts present in countries in this field and close to our professional activity have been included.

Ultrasound evaluation of the median nerve in carpal tunnel syndrome (CTS) is inherently operator-dependent. Automating CTS severity grading from ultrasound using deep learning may improve objectivity, reproducibility, and scalability in clinical practice. Background/Objectives: To develop convolutional neural network (CNN) models for automated CTS severity classification using B-mode ultrasound and electrodiagnostic results as the reference standard, and to compare performance between ultrasound scans. Methods: Fifty participants with suspected CTS provided 94 wrists, each classified into four severity levels (normal, mild, moderate, severe) through standardized nerve conduction studies. Ultrasound videos in transverse and longitudinal planes were acquired under a uniform protocol. From 11,895 frames, expert quality control produced a dataset of 2,518 valid images. Two CNN classifiers (transverse and longitudinal) were trained on nerve-centred crops using a 75/25 training–validation split, Adam optimization, and categorical cross-entropy loss. Performance was assessed through accuracy, class-wise precision, and confusion matrices. Results: Both models showed stable convergence. The transverse classifier achieved 0.92 validation accuracy, with precision values of 0.88 (normal), 0.94 (mild), 0.93 (moderate), and 0.97 (severe). The longitudinal classifier achieved 0.94 accuracy, with precision values of 0.98 (normal), 0.94 (mild), 0.96 (moderate), and 0.85 (severe). Misclassifications occurred mainly between adjacent categories, and reduced performance for severe longitudinal cases reflected limited sample representation. Conclusions: CNN-based classifiers can automatically predict CTS severity from B-mode ultrasound with high agreement to electrodiagnostic labels. These operator-independent models may support standardized diagnostic pathways and ultrasound-based screening. Future work should expand and balance datasets, incorporate multi-center data, and conduct external validation.