Científicos afirman que las cifras de riesgo de desarrollar covid persistente que se han ofrecido son "exageradas"

A recent critical study published in *BMJ Evidence Based Medicine* questions the validity of global estimates on the prevalence of persistent COVID-19, arguing that much of the existing scientific evidence suffers from serious methodological biases. The researchers point out that the broad definition used by international organizations and the lack of adequate control groups in many trials have distorted risk assessments, potentially exaggerating the actual impact of the condition. This raises concerns about possible negative consequences, such as unjustified increases in public anxiety and the misallocation of healthcare resources toward erroneous diagnoses. The relevance of this analysis for the field of open data lies in the critical need for transparency and rigor in the management and publication of scientific evidence. The article highlights how the quality of underlying data—specifically the methodology of clinical studies registered in public databases such as the ICTRP—determines the reliability of public health conclusions. If original data lack proper controls or exhibit sampling biases, any secondary analysis or data aggregation based on them will propagate systematic errors, compromising the integrity of accessible science. To move forward, the authors propose abandoning the general term "persistent COVID" in favor of stricter, more specific definitions that require demonstrated causal links with the virus, thereby improving methodological standards. This stance underscores that continuous improvement in the criteria for generating and publishing data is essential to adequately support patients and prevent inappropriate treatments. In an open information ecosystem, this case demonstrates that without clean, methodologically sound data, transparency can amplify rather than resolve health-related uncertainty.

Source: larazon.es
Published on 2023-09-27

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