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Bibliographic Details
Main Authors: Anaya Torres, Alejandra, Ramos-Martínez, Juan Carlos, Flores Marín, Lidya Paola, Luquín Pulido, José Refugio, Ramos-Martínez, Edgar Gustavo
Format: Recurso digital
Language:English
Published: Zenodo 2025
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Online Access:https://doi.org/10.5281/zenodo.19906213
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  • <p class="MsoNormal"><strong><span>Description</span></strong></p> <p class="MsoNormal"><span>This dataset contains anonymized clinical and demographic data from <strong>267 patients diagnosed with acute ischemic stroke who received intravenous thrombolysis within the therapeutic window</strong> at Hospital General de Zona No. 33 of the Instituto Mexicano del Seguro Social (IMSS), Monterrey, Mexico, during 2024.</span></p> <p class="MsoNormal"><span>The dataset was generated as part of a retrospective observational and analytical cohort study designed to evaluate the <strong>therapeutic effectiveness of systemic thrombolysis at 24 hours</strong>, measured using the National Institutes of Health Stroke Scale (NIHSS).</span></p> <p class="MsoNormal"><span>The primary outcome was defined as <strong>significant neurological improvement</strong>, operationalized as a reduction of ≥4 points in NIHSS score 24 hours after thrombolytic treatment.</span></p> <p class="MsoNormal"><span>Clinical, laboratory, and treatment-related variables were collected from electronic medical records, including demographic characteristics, comorbidities, time intervals related to thrombolysis, laboratory results, stroke severity indicators, and treatment outcomes.</span></p> <p class="MsoNormal"><span>The dataset supports reproducibility of statistical analyses presented in the thesis:</span></p> <p class="MsoNormal"><strong>"Efectividad terapéutica a las 24 horas de la trombólisis sistémica en pacientes con evento vascular cerebral isquémico en ventana terapéutica en el Hospital General de Zona No. 33 de Monterrey"</strong></p> <p class="MsoNormal"><span>The dataset may be reused for:</span></p> <ul> <li class="MsoNormal">Clinical outcome prediction models</li> <li class="MsoNormal">Stroke epidemiology research</li> <li class="MsoNormal">Health services performance evaluation</li> <li class="MsoNormal"><span>Machine learning models in stroke prognosis </span></li> <li class="MsoNormal">Secondary statistical analyses</li> <li class="MsoNormal">Meta-analyses involving thrombolysis effectiveness</li> </ul> <p class="MsoNormal"><span>All data were anonymized prior to analysis, ensuring no identifiable patient information is included.</span></p>