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Sökning: WFRF:(Krause Diana N.) > (2021)

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1.
  • Krause, Diana N., et al. (författare)
  • Hormonal influences in migraine — interactions of oestrogen, oxytocin and CGRP
  • 2021
  • Ingår i: Nature Reviews Neurology. - : Springer Science and Business Media LLC. - 1759-4758 .- 1759-4766. ; 17:10, s. 621-633
  • Forskningsöversikt (refereegranskat)abstract
    • Migraine is ranked as the second highest cause of disability worldwide and the first among women aged 15–49 years. Overall, the incidence of migraine is threefold higher among women than men, though the frequency and severity of attacks varies during puberty, the menstrual cycle, pregnancy, the postpartum period and menopause. Reproductive hormones are clearly a key influence in the susceptibility of women to migraine. A fall in plasma oestrogen levels can trigger attacks of migraine without aura, whereas higher oestrogen levels seem to be protective. The basis of these effects is unknown. In this Review, we discuss what is known about sex hormones and their receptors in migraine-related areas in the CNS and the peripheral trigeminovascular pathway. We consider the actions of oestrogen via its multiple receptor subtypes and the involvement of oxytocin, which has been shown to prevent migraine attacks. We also discuss possible interactions of these hormones with the calcitonin gene-related peptide (CGRP) system in light of the success of anti-CGRP treatments. We propose a simple model to explain the hormone withdrawal trigger in menstrual migraine, which could provide a foundation for improved management and therapy for hormone-related migraine in women.
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2.
  • Schweinsberg, Martin, et al. (författare)
  • Same data, different conclusions : Radical dispersion in empirical results when independent analysts operationalize and test the same hypothesis
  • 2021
  • Ingår i: Organizational Behavior and Human Decision Processes. - : Elsevier BV. - 0749-5978 .- 1095-9920. ; 165, s. 228-249
  • Tidskriftsartikel (refereegranskat)abstract
    • In this crowdsourced initiative, independent analysts used the same dataset to test two hypotheses regarding the effects of scientists' gender and professional status on verbosity during group meetings. Not only the analytic approach but also the operationalizations of key variables were left unconstrained and up to individual analysts. For instance, analysts could choose to operationalize status as job title, institutional ranking, citation counts, or some combination. To maximize transparency regarding the process by which analytic choices are made, the analysts used a platform we developed called DataExplained to justify both preferred and rejected analytic paths in real time. Analyses lacking sufficient detail, reproducible code, or with statistical errors were excluded, resulting in 29 analyses in the final sample. Researchers reported radically different analyses and dispersed empirical outcomes, in a number of cases obtaining significant effects in opposite directions for the same research question. A Boba multiverse analysis demonstrates that decisions about how to operationalize variables explain variability in outcomes above and beyond statistical choices (e.g., covariates). Subjective researcher decisions play a critical role in driving the reported empirical results, underscoring the need for open data, systematic robustness checks, and transparency regarding both analytic paths taken and not taken. Implications for orga-nizations and leaders, whose decision making relies in part on scientific findings, consulting reports, and internal analyses by data scientists, are discussed.
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