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7 result(s) for “ip cram site here com site examine com site examine com site doi org site:pmc.ncbi.nlm.nih.gov” · 679 ms
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Given these recent findings outlined above, it seems pertinent to further examine whether IU proves to be a more sensitive predictor of compromised fear extinction, over general trait anxiety measures such as the STAI
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https://pmc.ncbi.nlm.nih.gov/articles/PMC5154327/
Here we sought to determine if individual differences in Intolerance of Uncertainty (IU), a potential risk factor for anxiety disorders, underlies compromised fear extinction
https://pmc.ncbi.nlm.nih.gov/articles/PMC4430551/
Linear-regression models were used to examine the relationship between declining pollution levels over time and lung-function development from 11 to 15 years of age, measured as the increases in forced expiratory volume in 1 second (FEV1) and forced vital capacity (FVC) during that period (referred to as 4-year growth
https://pmc.ncbi.nlm.nih.gov/articles/PMC1240435/
In this paper we examine the implications of the precautionary principle for environmental scientists, whose work often involves studying highly complex, poorly understood systems, while at the same time facing conflicting pressures from those who seek to balance economic growth and environmental protection
https://pmc.ncbi.nlm.nih.gov/articles/PMC4255271/
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https://pmc.ncbi.nlm.nih.gov/articles/PMC5019949/
within six metropolitan areas in the USA (the Multi-Ethnic Study of Atherosclerosis and Air Pollution): a longitudinal cohort study - PMC
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https://pmc.ncbi.nlm.nih.gov/articles/PMC10718082/
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In this commentary, we present an approach to examine whether formal identification of a substance as a human carcinogen may prompt a regulatory action to reduce exposure to carcinogens and enhance public health
https://pmc.ncbi.nlm.nih.gov/api/oai/v1/mh/?verb=ListRecords&from=2025-03-22&until=2025-06-12&set=bmj&metadataPrefix=pmc
PROBAST+AI may replace the original PROBAST tool and allows all key stakeholders (eg, model developers, AI companies, researchers, editors, reviewers, healthcare professionals, guideline developers, and policy organisations) to examine the quality, risk of bias, and applicability of any type of prediction model in the