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- Pereira, Susana, et al.
(författare)
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Is the fetus fit for labor? Introducing fast-and-frugal trees (FFTrees) to simplify triage of women for STAN monitoring : An interobserver agreement comparison with traditional classification
- 2024
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Ingår i: Acta Obstetricia et Gynecologica Scandinavica. - 0001-6349. ; 103:1, s. 68-76
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Tidskriftsartikel (refereegranskat)abstract
- Introduction: It is a shortcoming of traditional cardiotocography (CTG) classification table formats that CTG traces are frequently classified differently by different users, resulting in poor interobserver agreements. A fast-and-frugal tree (FFTree) flow chart may help provide better concordance because it is straightforward and has clearly structured binary questions with understandable “yes” or “no” responses. The initial triage to determine whether a fetus is suitable for labor when utilizing fetal ECG ST analysis (STAN) is very important, since a fetus with restricted capacity to respond to hypoxic stress may not generate STAN events and therefore may become falsely negative. This study aimed to compare physiology-focused FFTree CTG interpretation with FIGO classification for assessing the suitability for STAN monitoring. Material and methods: A retrospective study of 36 CTG traces with a high proportion of adverse outcomes (17/36) selected from a European multicenter study database. Eight experienced European obstetricians evaluated the initial 40 minutes of the CTG recordings and judged whether STAN was a suitable fetal surveillance method and whether intervention was indicated. The experts rated the CTGs using the FFTree and FIGO classifications at least 6 weeks apart. Interobserver agreements were calculated using proportions of agreement and Fleiss’ kappa (κ). Results: The proportions of agreement for “not suitable for STAN” were for FIGO 47% (95% confidence interval [CI] 42%–52%) and for FFTree 60% (95% CI 56–64), ie a significant difference; the corresponding figures for “yes, suitable” were 74% (95% CI 71–77) and 70% (95% CI 67–74). For “intervention needed” the figures were 52% (95% CI 47–56) vs 58% (95% CI 54–62) and for “expectant management” 74% (95% CI 71–77) vs 72% (95% CI 69–75). Fleiss’ κ agreement on “suitability for STAN” was 0.50 (95% CI 0.44–0.56) for the FIGO classification and 0.57 (95% CI 0.51–0.63) for the FFTree classification; the corresponding figures for “intervention or expectancy” were 0.53 (95% CI 0.47–0.59) and 0.57 (95% CI 0.51–0.63). Conclusions: The proportion of agreement among expert obstetricians using the FFTree physiological approach was significantly higher compared with the traditional FIGO classification system in rejecting cases not suitable for STAN monitoring. That might be of importance to avoid false negative STAN recordings. Other agreement figures were similar. It remains to be shown whether the FFTree simplicity will benefit less experienced users and how it will work in real-world clinical scenarios.
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| 3. |
- Wortel, Meike T., et al.
(författare)
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Towards evolutionary predictions : current promises and challenges
- 2023
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Ingår i: Evolutionary Applications. - : John Wiley & Sons. - 1752-4571. ; 16:1, s. 3-21
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Forskningsöversikt (refereegranskat)abstract
- Evolution has traditionally been a historical and descriptive science, and predicting future evolutionary processes has long been considered impossible. However, evolutionary predictions are increasingly being developed and used in medicine, agriculture, biotechnology and conservation biology. Evolutionary predictions may be used for different purposes, such as to prepare for the future, to try and change the course of evolution or to determine how well we understand evolutionary processes. Similarly, the exact aspect of the evolved population that we want to predict may also differ. For example, we could try to predict which genotype will dominate, the fitness of the population or the extinction probability of a population. In addition, there are many uses of evolutionary predictions that may not always be recognized as such. The main goal of this review is to increase awareness of methods and data in different research fields by showing the breadth of situations in which evolutionary predictions are made. We describe how diverse evolutionary predictions share a common structure described by the predictive scope, time scale and precision. Then, by using examples ranging from SARS-CoV2 and influenza to CRISPR-based gene drives and sustainable product formation in biotechnology, we discuss the methods for predicting evolution, the factors that affect predictability and how predictions can be used to prevent evolution in undesirable directions or to promote beneficial evolution (i.e. evolutionary control). We hope that this review will stimulate collaboration between fields by establishing a common language for evolutionary predictions.
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