Madrid Health Service

Objetivo: evaluar la prevalencia y factores asociados a la prescripción crónica inadecuada de inhibidores de la bomba de protones (IBP) en Atención Primaria para proponer intervenciones específicas que mejoren la adecuación terapéutica. Métodos: estudio descriptivo transversal realizado en un centro de salud urbano en la Comunidad de Madrid. Se incluyeron pacientes mayores de 18 años con prescripción crónica de IBP registrados en el Módulo Único de Prescripción (MUP). La adecuación se determinó según la evidencia científica disponible. Se evaluaron variables demográficas y clínicas mediante análisis descriptivo, bivariado y regresión logística multivariada. Resultados: se incluyeron 380 pacientes (media edad: 69,7; desviación estándar [DE]: 14,05 años; 58,15% mujeres; 83,15% polimedicados). La prescripción crónica inadecuada de IBP fue del 65% (intervalo de confianza [IC] 95%: 60,0-69,6%). La principal causa de inadecuación fue una indicación incorrecta (81,8%), seguida de temporalidad inadecuada (18,2%). E

Background and Objectives: Long COVID-19 (LC) is a multifaceted condition characterized by persistent fatigue and impaired health-related quality of life (HRQoL). Exercise intolerance and post-exertional symptom exacerbation (PESE) pose challenges for rehabilitation. This study aimed to evaluate the effects of a 12-week core-focused plank exercise program on fatigue and HRQoL in women with LC, using validated patient-reported measures. Materials and Methods: A pilot quasi-experimental design was implemented, with non-randomized group allocation. Thirty-nine women with LC were recruited from the Madrid Long COVID Association. Participants were assigned to either an intervention group (n = 20), which completed a supervised plank-based motor control program, or a control group (n = 19), which maintained usual activity. Fatigue was assessed using the Modified Fatigue Impact Scale (MFIS), and HRQoL was measured using the EQ-5D-5L and EQ Visual Analog Scale (EQ-VAS). Body composition was evaluated via bioelectrical impedance analysis. Results: The intervention group showed significant reductions after intervention in the MFIS total scores compared to the control group, particularly in the physical (21.26 ± 6.76 vs. 25.21 ± 6.06; p < 0.001) and psychosocial domains (4.51 ± 0.41 vs. 5.21 ± 0.38; p < 0.001), without triggering PESE. EQ-VAS scores improved significantly (63.94 ± 15.33 vs. 46.31 ± 14.74; p = 0.034). No significant changes were found in body composition parameters, suggesting that benefits were driven by neuromuscular adaptations rather than morphological changes. Conclusions: A core-focused, non-aerobic exercise program effectively reduced fatigue and improved perceived health status in women with LC. These findings support the use of motor control-based interventions as a safe and feasible strategy for LC rehabilitation, particularly in populations vulnerable to PESE, suggesting clinical applicability for the rehabilitation of women with LC. Further randomized trials are warranted to confirm these results and explore long-term outcomes.

BackgroundIn recent years, generative artificial intelligence and large language models (LLMs) have rapidly advanced, offering significant potential to transform medical education. Several studies have evaluated the performance of chatbots on multiple-choice medical examinations.ObjectiveThe study aims to assess the performance of two LLMs—GPT-4o and OpenAI o1—on the Médico Interno Residente (MIR) 2024 examination, the Spanish national medical test that determines eligibility for competitive medical specialist training positions.MethodsA total of 176 questions from the MIR 2024 examination were analyzed. Each question was presented individually to the chatbots to ensure independence and prevent memory retention bias. No additional prompts were introduced to minimize potential bias. For each LLM, response consistency under verification prompting was assessed by systematically asking, “Are you sure?” after each response. Accuracy was defined as the percentage of correct responses compared to the official answers provided by the Spanish Ministry of Health. It was assessed for GPT-4o, OpenAI o1, and, as a benchmark, for a consensus of medical specialists and for the average MIR candidate. Subanalyses included performance across different medical subjects, question difficulty (quintiles based on the percentage of examinees correctly answering each question), and question types (clinical cases vs theoretical questions; positive vs negative questions).ResultsOverall accuracy was 89.8% (158/176) for GPT-4o and 90% (160/176) after verification prompting, 92.6% (163/176) for OpenAI o1 and 93.2% (164/176) after verification prompting, 94.3% (166/176) for the consensus of medical specialists, and 56.6% (100/176) for the average MIR candidate. Both LLMs and the consensus of medical specialists outperformed the average MIR candidate across all 20 medical subjects analyzed, with ≥80% LLMs’ accuracy in most domains. A performance gradient was observed: LLMs’ accuracy gradually declined as question difficulty increased. Slightly higher accuracy was observed for clinical cases compared to theoretical questions, as well as for positive questions compared to negative ones. Both models demonstrated high response consistency, with near-perfect agreement between initial responses and those after the verification prompting.ConclusionsThese findings highlight the excellent performance of GPT-4o and OpenAI o1 on the MIR 2024 examination, demonstrating consistent accuracy across medical subjects and question types. The integration of LLMs into medical education presents promising opportunities and is likely to reshape how students prepare for licensing examinations and change our understanding of medical education. Further research should explore how the wording, language, prompting techniques, and image-based questions can influence LLMs’ accuracy, as well as evaluate the performance of emerging artificial intelligence models in similar assessments.

Abstract Introduction Bicuspid aortic valves (BAV) exist in different valvular morphotypes. A recent International Consensus Classification and Nomenclature [1] was introduced, and includes 3 types of BAV: (i) the 3-sinus fused type, additionally divided into right-left (RL), right-non-coronary (RN) and left-non-coronary (LN) cusp fusion types, (ii) the 2-sinus type, divided into latero-lateral (LL) and antero-posterior (AP) forms, and (iii) the partial-fusion (or forme fruste) type. This classification serves as unified criteria to identify BAV phenotypes, possibly enabling improved prognostic and therapeutic considerations and a better knowledge of disease progression and clinical outcomes. Purpose To quantify the relative prevalence of BAV morphotypes following this recent classification and test the diagnostic accuracy as well as the inter-observer agreement on computed tomography (CT) and transthoracic echocardiography (TTE). Methods This is a secondary analysis of patients included in a recent clinical trial that included the acquisition of both TTE and contrast-enhanced CT [2]. Two blinded observers identified BAV morphotypes accounting for the number of Valsalva sinuses by CT. In case of disagreement, by discussion and consensus. Two observers also identified the number of sinuses, the fusion phenotypes and the presence of partial fusion in TTE studies. Results Data from 146 patients from 9 clinical centers were used. Patients were mostly middle-aged (median and inter-quartile age 45 [38-53] years) men (81%). Nineteen patients (13%) showed a two-sinus BAV. On CT, a 92.5% inter-observer agreement on the presence of 2 vs 3-sinus BAV was obtained. As compared with CT-based consensus, the diagnostic accuracy for 2 vs 3-sinus BAV on TTE was 88.4%. The errors consisted of missing 2-sinus BAV (over 17 errors, 16 (94%) where missing a 2-sinus form), resulting in a low sensitivity, as only 16% of 2-sinus BAV were correctly identified. Of note, both observers missed the same 16 2-sinus BAV on TTE, thus resulting in a 100% inter-observer agreement. The inter-observer agreement of 3-sinus fusion types on TTE was 93%, with disagreement mainly due to differences between RL and RN fusions (7/10 disagreements, 70%). The inter-observer agreement of partial fusion on TTE was 96.6%, with only 5 patients classified differently by the two experts. Conclusions The inter-observer agreement in the application of the new BAV International Consensus Classification and Nomenclature by TTE is high. However, given that a substantial number of 2-sinus BAV are missed by TTE, multi-modal imaging should be considered when this distinction has clinical relevance for decision-making.

Frequently, public service deliverance has adverse substantive impacts on the environment. Wolf’s theory of non-market failure views these impacts as derived externalities , while Moore’s theory of public value invites to view them as resulting from a partial or biased consideration of the public-value dimensions by the service providers. In the case of healthcare, such environmental damages also have a boomerang effect, so they often affect people’s health. However, the carbon footprint (CF) of public hospitals remains understudied in many countries. More specifically, very little research focuses on the evolution of CF at the organizational (hospital) level under a systemic shock like the last pandemic. To our knowledge, this is the first study that combines CF computation and data envelopment analysis (DEA) to investigate the environmental efficiency of public hospitals before and during the pandemic. We compare a big and purely public hospital (HA) with a smaller public hospital managed through a public-private partnership (HB). In both hospitals, the main sources of emissions were electricity consumption (55.89% in HA and 48.41% in HB) and natural gas (28.89% in HA and 26.41% in HB). During the pandemic, the two hospitals achieved higher scores in our DEA-based environmental efficiency measure, even when a standard general activity volume indicator is added as input. Our results portray 2020 as the abrupt shock year and 2021 as the year of a shock attenuation (HA) or partial return to normalcy (HB).