Análisis de la carga laboral en tiempo médico en función del grado de complejidad de las muestras y propuesta de algoritmo para la distribución equitativa del trabajo asistencial en un servicio de anatomía patológica / Analysis of the workload in medical time according to the degree of complexity of the samples and proposal of an algorithm for the equitable distribution of the workload in an anatomic pathology department

Introducción: En un servicio de anatomía patológica se analiza la carga laboral en tiempo médico en función de la complejidad de las muestras recibidas, y se valora su distribución entre los patólogos, presentado un nuevo algoritmo informático que favorece una distribución equitativa. Métodos: Siguiendo las directrices para la «Estimación de la carga de trabajo en citopatología e histopatología (tiempo médico) atendiendo al catálogo de muestras y procedimientos de la SEAP-IAP (2.ª edición)» se determinan las unidades de carga laboral (UCL) por patólogo y UCL global del servicio, la carga media laboral que soporta el servicio (factor MU), el tiempo de dedicación de cada patólogo a la actividad asistencial y el número de patólogos óptimo según la carga laboral del servicio. Resultados: Determinamos 12.197 UCL totales anuales para el patólogo jefe de servicio, así como 14.702 y 13.842 para los patólogos adjuntos, con una UCL global del servicio de 40.742. El factor MU calculado es 4,97. El jefe ha dedicado el 72,25% de su jornada a la asistencia y los adjuntos el 87,09 y 82,01%. El número de patólogos óptimo para el servicio es de 3,55. Conclusiones: Todos los resultados obtenidos demuestran la sobrecarga laboral médica, y la distribución de las UCL entre los patólogos no resulta equitativa. Se propone un algoritmo informático capaz de distribuir la carga laboral de manera equitativa, asociado al sistema de información del laboratorio, y que tenga en cuenta el tipo de muestra, su complejidad y la dedicación asistencial de cada patólogo.(AU)

Introduction: In a pathological anatomy service, the workload in medical time is analyzed based on the complexity of the samples received and its distribution among pathologists is assessed, presenting a new computer algorithm that favors an equitable distribution. Methods: Following the second edition of the Spanish guidelines for the estimation of workload in cytopathology and histopathology (medical time) according to the Spanish Pathology Society-International Academy of Pathology (SEAP-IAP) catalog of samples and procedures, we determined the workload units (UCL) per pathologist and the overall UCL of the service, the average workload of the service (MU factor), the time dedicated by each pathologist to healthcare activity and the optimal number of pathologists according to the workload of the service. Results: We determined 12 197 total annual UCL for the chief pathologist, as well as 14 702 and 13 842 UCL for associate pathologists, with an overall of 40 742 UCL for the whole service. The calculated MU factor is 4.97. The chief pathologist devoted 72.25% of his working day to healthcare activity while associate pathologists dedicated 87.09% and 82.01% of their working hours. The optimal number of pathologists for the service is found to be 3.55. Conclusions: The results demonstrate medical work overload and a non-equitable distribution of UCLs among pathologists. We propose a computer algorithm capable of distributing the workload in an equitable manner. It would be associated with the laboratory information system and take into account the type of specimen, its complexity and the dedication of each pathologist to healthcare activity.(AU)

ChatGPT es un estudiante por encima de la media en la Facultad de Medicina de la Universidad de Zaragoza y un colaborador excelente en la elaboración de material docente / ChatGPT is an above-average student at the Faculty of Medicine of the University of Zaragoza and an excellent collaborator in the development of teaching materials

Introducción y objetivo: La inteligencia artificial se halla plenamente presente en nuestras vidas. En educación las posibilidades de su uso son infinitas, tanto para alumnos como para docentes. Material y métodos: Se ha explorado la capacidad de ChatGPT a la hora de resolver preguntas tipo test a partir del examen de la asignatura Procedimientos Diagnósticos y Terapéuticos Anatomopatológicos de la primera convocatoria del curso 2022-2023. Además de comparar su resultado con el del resto de alumnos presentados, se han evaluado las posibles causas de las respuestas incorrectas. Finalmente, se ha evaluado su capacidad para realizar preguntas de test nuevas a partir de instrucciones específicas. Resultados: ChatGPT ha acertado 47 de las 68 preguntas planteadas, obteniendo una nota superior a la de la media y mediana del curso. La mayor parte de preguntas falladas presentan enunciados negativos, utilizando las palabras «no», «falsa» o «incorrecta» en su enunciado. Tras interactuar con él, el programa es capaz de darse cuenta de su error y cambiar su respuesta inicial por la correcta. Finalmente, ChatGPT sabe elaborar nuevas preguntas a partir de un supuesto teórico o bien de una simulación clínica determinada. Conclusiones: Como docentes estamos obligados a explorar las utilidades de la inteligencia artificial, e intentar usarla en nuestro beneficio. La realización de tareas que suponen un consumo de tipo importante, como puede ser la elaboración de preguntas tipo test para evaluación de contenidos, es un buen ejemplo. (AU)

Introduction and objective: Artificial intelligence is fully present in our lives. In education, the possibilities of its use are endless, both for students and teachers. Material and methods: The capacity of ChatGPT has been explored when solving multiple choice questions based on the exam of the subject «Anatomopathological Diagnostic and Therapeutic Procedures» of the first call of the 2022-23 academic year. In addition, to comparing their results with those of the rest of the students presented the probable causes of incorrect answers have been evaluated. Finally, its ability to formulate new test questions based on specific instructions has been evaluated. Results: ChatGPT correctly answered 47 out of 68 questions, achieving a grade higher than the course average and median. Most failed questions present negative statements, using the words «no», «false» or «incorrect» in their statement. After interacting with it, the program can realize its mistake and change its initial response to the correct answer. Finally, ChatGPT can develop new questions based on a theoretical assumption or a specific clinical simulation. Conclusions: As teachers we are obliged to explore the uses of artificial intelligence and try to use it to our benefit. Carrying out tasks that involve significant consumption, such as preparing multiple-choice questions for content evaluation, is a good example. (AU)

Use of Artificial Intelligence Chatbots in Interpretation of Pathology Reports.

Importance: Anatomic pathology reports are an essential part of health care, containing vital diagnostic and prognostic information. Currently, most patients have access to their test results online. However, the reports are complex and are generally incomprehensible to laypeople. Artificial intelligence chatbots could potentially simplify pathology reports. Objective: To evaluate the ability of large language model chatbots to accurately explain pathology reports to patients. Design, Setting, and Participants: This cross-sectional study used 1134 pathology reports from January 1, 2018, to May 31, 2023, from a multispecialty hospital in Brooklyn, New York. A new chat was started for each report, and both chatbots (Bard [Google Inc], hereinafter chatbot 1; GPT-4 [OpenAI], hereinafter chatbot 2) were asked in sequential prompts to explain the reports in simple terms and identify key information. Chatbot responses were generated between June 1 and August 31, 2023. The mean readability scores of the original and simplified reports were compared. Two reviewers independently screened and flagged reports with potential errors. Three pathologists reviewed the flagged reports and categorized them as medically correct, partially medically correct, or medically incorrect; they also recorded any instances of hallucinations. Main Outcomes and Measures: Outcomes included improved mean readability scores and a medically accurate interpretation. Results: For the 1134 reports included, the Flesch-Kincaid grade level decreased from a mean of 13.19 (95% CI, 12.98-13.41) to 8.17 (95% CI, 8.08-8.25; t = 45.29; P < .001) by chatbot 1 and 7.45 (95% CI, 7.35-7.54; t = 49.69; P < .001) by chatbot 2. The Flesch Reading Ease score was increased from a mean of 10.32 (95% CI, 8.69-11.96) to 61.32 (95% CI, 60.80-61.84; t = -63.19; P < .001) by chatbot 1 and 70.80 (95% CI, 70.32-71.28; t = -74.61; P < .001) by chatbot 2. Chatbot 1 interpreted 993 reports (87.57%) correctly, 102 (8.99%) partially correctly, and 39 (3.44%) incorrectly; chatbot 2 interpreted 1105 reports (97.44%) correctly, 24 (2.12%) partially correctly, and 5 (0.44%) incorrectly. Chatbot 1 had 32 instances of hallucinations (2.82%), while chatbot 2 had 3 (0.26%). Conclusions and Relevance: The findings of this cross-sectional study suggest that artificial intelligence chatbots were able to simplify pathology reports. However, some inaccuracies and hallucinations occurred. Simplified reports should be reviewed by clinicians before distribution to patients.

Educational climate of a pathology residency program at a tertiary care hospital.

Evaluating educational climate (EC) is imperative for ensuring postgraduate trainees' competencies and quality in residency training programs. This study assessed the EC experiences of pathology postgraduate residents (PGRs) during their postgraduate training in pathology residency programs-a cross-sectional study design assigned EC scores in the pathology residency program at a prestigious institution in Islamabad, which were measured using the Dutch Residency Educational Climate Test (D-RECT) questionnaire. Scores from the D-RECT were employed to conduct descriptive statistics and comparison of means across groups to evaluate EC scores by years of training and compared to assess where the differences were located. Among FCPS-II pathology residents, most of whom were females (94.4%), the mean age was 28.11±2.91 years. A mean positive score was observed among all pathology residents (M≥3.6) for all D-RECT subscales except for the feedback subscale: the average score for feedback was below the average mean score of 3.6 (M = 3.19). A significant difference p = 0.016 was observed in EC scores across different groups through the Analysis of Variances (ANOVA) test. The most significant difference was between less than two and greater than two groups p = 0.027, followed by the difference between equal to two groups and greater than two groups p = 0.052. Overall, positive scores for EC in the pathology residency program were observed. Thus, targeted interventions are needed to increase feedback scores and address observed differences in EC scores by years of training.

Forgotten clientele: A systematic review of patient-centered pathology reports.

CONTEXT: Patient portals, designed to give ready access to medical records, have led to important improvements in patient care. However, there is a downside: much of the information available on portals is not designed for lay people. Pathology reports are no exception. Access to complex reports often leaves patients confused, concerned and stressed. We conducted a systematic review to explore recommendations and guidelines designed to promote a patient centered approach to pathology reporting. DESIGN: In consultation with a research librarian, a search strategy was developed to identify literature regarding patient-centered pathology reports (PCPR). Terms such as "pathology reports," "patient-centered," and "lay-terms" were used. The PubMed, Embase and Scopus databases were searched during the first quarter of 2023. Studies were included if they were original research and in English, without date restrictions. RESULTS: Of 1,053 articles identified, 17 underwent a full-text review. Only 5 studies (≈0.5%) met eligibility criteria: two randomized trials; two qualitative studies; a patient survey of perceived utility of potential interventions. A major theme that emerged from the patient survey/qualitative studies is the need for pathology reports to be in simple, non-medical language. Major themes of the quantitative studies were that patients preferred PCPRs, and patients who received PCPRs knew and recalled their cancer stage/grade better than the control group. CONCLUSION: Pathology reports play a vital role in the decision-making process for patient care. Yet, they are beyond the comprehension of most patients. No framework or guidelines exist for generating reports that deploy accessible language. PCPRs should be a focus of future interventions to improve patient care.

[Comparative pathology in oncology-Best practice]. / Vergleichende Pathologie in der onkologischen Forschung.

Comparative experimental pathology is a research field at the interface of human and veterinary medicine. It is focused on the comparative study of similarities and differences between spontaneous and experimentally induced diseases in animals (animal models) compared to human diseases. The use of animal models for studying human diseases is an essential component of biomedical research. Interdisciplinary teams with species-specific expertise should collaborate wherever possible and maintain close communication. Mutual openness, cooperation, and willingness to learn form the basis for a fruitful collaboration. Research projects jointly led by or involving both animal and human pathologists make a significant contribution to high-quality biomedical research. Such approaches are promising not only in oncological research, as outlined in this article, but also in other research areas where animal models are regularly used, such as infectiology, neurology, and developmental biology.

[Accelerating the construction of digital and intelligentialized pathology and the prospects].

With the continuous development of informatization, digitalization and artificial intelligence technology, the working mode of the pathology department has gradually changed from the traditional manual check, paper circulation and physical carrier storage to the informatization process and digital storage. The traditional pathology discipline has ushered in unprecedented opportunities and challenges. Digital pathology department also emerge as the times require. Simultaneously, with the full integration of artificial intelligence technology in pathology department, the concept of "department of digital and intelligentialized pathology" was proposed. Based on information and digital technology, the digital intelligent pathology department integrates intelligent management system, optimizes the previous cumbersome management and workflow of the pathology department, develops advanced technologies such as intelligent material extraction, unmanned organization processing, artificial intelligence quality control, artificial intelligence diagnosis, and promotes the intelligent construction of the pathology department.

GPX4, ferroptosis, and diseases.

GPX4 (Glutathione peroxidase 4) serves as a crucial intracellular regulatory factor, participating in various physiological processes and playing a significant role in maintaining the redox homeostasis within the body. Ferroptosis, a form of iron-dependent non-apoptotic cell death, has gained considerable attention in recent years due to its involvement in multiple pathological processes. GPX4 is closely associated with ferroptosis and functions as the primary inhibitor of this process. Together, GPX4 and ferroptosis contribute to the pathophysiology of several diseases, including sepsis, nervous system diseases, ischemia reperfusion injury, cardiovascular diseases, and cancer. This review comprehensively explores the regulatory roles and impacts of GPX4 and ferroptosis in the development and progression of these diseases, with the aim of providing insights for identifying potential therapeutic strategies in the future.

Efeito da fotobiomodulação na reabilitação de pacientes com patologias traumato-ortopédicas: revisão integrativa / Efectos de la fotobiomodulación en la rehabilitación de pacientes con patologías traumatoortopédicas: revisión integrativa / Effects of photobiomodulationin the rehabilitation of patients with traumato-orthopedic pathologies: integrative review

Introdução: Fotobiomodulação corresponde à exposição de tecidos biológicos a baixos níveis de luz vermelha e infravermelha, esta terapia favorece a reabilitação de diferentes tecidos e que pode ser utilizada para a melhora da prática clínica nas diferentes atuações da fisioterapia, como por exemplo, no tratamento dos acometimentos musculoesqueléticos e inflamatórios. Objetivo: Identificar os benefícios da fotobiomodulação empregados na reabilitação de pacientes nas diferentes patologias traumato-ortopédicas. Metodologia:Trata-se de uma revisão integrativa com busca online nas plataformas de dados: Medline e PubMed. Os descritores foram: fotobiomodulação, taumato-ortopedia e reabilitação. As línguas selecionadas foram: Português, Inglês e Espanhol, entre os anos de 2018 a 2022.Resultados:A descrição dos achados nos ensaios clínicos analisados mostra que a terapia de fotobiomodulação apresenta diferentes usos na prática clínica e que seu uso produz efeito analgésico, anti-inflamatório e regenerativo nos distúrbios musculoesqueléticos. Conclusões:De acordo com a revisão dos artigos, pôde-se perceber que a terapia por fotobiomodulação confirma seus benefícios e eficácia, portando, se fazendo positiva na atuação traumato-ortopédica, gerando resultados significativos quando comparada a outros recursos (AU).

Introduction: Photobiomodulationcorresponds to the exposure of biological tissues to low levels of red and infrared light, this therapy favors the rehabilitation of different tissues and can be used to improve clinical practice in different actions of physiotherapy, such as, for example, in the treatment of musculoskeletal and inflammatory disorders. Objective: To identify the benefits of photobiomodulation used in the rehabilitation of patients with different traumato-orthopedic pathologies. Methodology:This is an integrative review with online search on data platforms: Medline and PubMed. The descriptors were: photobiomodulation, thaumato-orthopedics and rehabilitation. The selected languages were: Portuguese, English and Spanish, from 2018 to 2022. Results: The description of the discovers in the analyzed clinical trials shows that photobiomodulation therapy has different uses in clinical practice and that its use produces analgesic, anti-inflammatory and regenerative effects in musculoskeletal disorders. Conclusions:According to the review of the articles, it could be seen that photobiomodulation therapy confirms its benefits and effectiveness, therefore, becoming positive in the trauma-orthopedic performance, generating significant results when compared to other resources (AU).

Introducción: La fotobiomodulacióncorresponde a la exposición de tejidos biológicos a bajos niveles de luz roja e infrarroja, esta terapia estimula la rehabilitación de diferentes tejidos y puede ser utilizada para mejorar la práctica clínica en diferentes áreas de fisioterapia, como por ejemplo, en tratamiento de Trastornos musculoesqueléticos e inflamatorios. Objetivo: Identificar los beneficios de la fotobiomodulación utilizada en la rehabilitación de pacientes con diferentes patologías traumato-ortopédicas. Metodología: Esta es una revisión integradora con búsqueda en línea en plataformas de datos: Medline y PubMed. Los descriptores fueron: fotobiomodulación, taumato-ortopedia y rehabilitación. Los idiomas seleccionados fueron: portugués, inglés y español, entre los años 2018 a 2022. Resultados: La descripción de los hallazgos en los ensayos clínicos analizados muestra que la terapia de fotobiomodulación tiene diferentes usos en la práctica clínica y que su uso produce efectos analgésicos, antiinflamatorios y regenerador en trastornos musculoesqueléticos. Conclusiones: De acuerdo con la revisión de los artículos, se pudo apreciar que la terapia de fotobiomodulación confirma sus beneficios y efectividad, por lo tanto, tornándose positiva en el desempeño trauma-ortopédico, generando resultados significativos cuando se compara con otros recursos (AU).

[Artificial intelligence for pathology-how, where, and why?] / Künstliche Intelligenz in der Pathologie ­ wie, wo und warum?

Artificial intelligence promises many innovations and simplifications in pathology, but also raises just as many questions and uncertainties. In this article, we provide a brief overview of the current status, the goals already achieved by existing algorithms, and the remaining challenges.

[Call attention to the overall benefit of digital pathology and promote its development].

The successful operation of the complete digital pathology(CDP) in foreign countries indicates that the full digital pathology process has entered the full implementation stage. Digital pathology in China started late and progressed slowly, so far there has not been a truly meaningful CDP. The pathologist's understanding of digital pathology is not comprehensive enough, and there are still doubts about the time efficiency and cost effectiveness of digital pathology processes. Therefore,a comprehensive analysis of the process, overall advantages and cost-effectiveness of CDP was made by drawing on international successful experience and hands-on practice experience, in order to promote the construction and development of the CDP in our country.

Attention De-sparsification Matters: Inducing diversity in digital pathology representation learning.

We propose DiRL, a Diversity-inducing Representation Learning technique for histopathology imaging. Self-supervised learning (SSL) techniques, such as contrastive and non-contrastive approaches, have been shown to learn rich and effective representations of digitized tissue samples with limited pathologist supervision. Our analysis of vanilla SSL-pretrained models' attention distribution reveals an insightful observation: sparsity in attention, i.e, models tends to localize most of their attention to some prominent patterns in the image. Although attention sparsity can be beneficial in natural images due to these prominent patterns being the object of interest itself, this can be sub-optimal in digital pathology; this is because, unlike natural images, digital pathology scans are not object-centric, but rather a complex phenotype of various spatially intermixed biological components. Inadequate diversification of attention in these complex images could result in crucial information loss. To address this, we leverage cell segmentation to densely extract multiple histopathology-specific representations, and then propose a prior-guided dense pretext task, designed to match the multiple corresponding representations between the views. Through this, the model learns to attend to various components more closely and evenly, thus inducing adequate diversification in attention for capturing context-rich representations. Through quantitative and qualitative analysis on multiple tasks across cancer types, we demonstrate the efficacy of our method and observe that the attention is more globally distributed.