The timing of amniotomy, oxytocin and neuraxial analgesia and its association with labour duration and mode of birth

Erworben im Rahmen der Schweizer Nationallizenzen (http://www.nationallizenzen.ch)Purpose: The objective was to study the association of different timings of intrapartum interventions with labour duration and mode of birth. Methods: A longitudinal cohort study of 2,090 nulliparae and 1,873 multiparae with a singleton in cephalic presentation was conducted. We assessed the association between, on the one hand, the timing of augmentation with oxytocin, neuraxial analgesia and amniotomy, and, on the other hand, the time to complete dilatation, spontaneous or operative vaginal delivery or caesarean delivery, using a Cox regression model accounting for standard confounders. Results: From amniotomy onwards labour was accelerated. In multiparae, amniotomy was associated with an initial 6.6-fold acceleration, decreasing first stage duration until the hazard ratio reached around 3.5, where the intervention was performed 5 h after labour onset; thereafter, acceleration continued with a hazard ratio of around 3. In nulliparae, neuraxial analgesia was associated with a shorter first stage when administered between 7 and 11 h after labour onset; the later it was performed, the less likely was spontaneous birth and the more likely an operative vaginal birth in nulliparae or a caesarean section in multiparae. The start of oxytocin augmentation was associated with acceleration towards both full dilatation and caesarean section during first stage and an increased risk of operative vaginal birth during second stage. The later oxytocin augmentation started, the more likely it was that spontaneous birth would be retarded in multiparous women. Conclusions: Applying amniotomy, oxytocin and neuraxial analgesia at their optimal timing may improve the progress and outcome of labour

Semi-Automated Image Analysis for the Assessment of Megafaunal Densities at the Arctic Deep-Sea Observatory HAUSGARTEN

Megafauna play an important role in benthic ecosystem function and are sensitive indicators of environmental change. Non-invasive monitoring of benthic communities can be accomplished by seafloor imaging. However, manual quantification of megafauna in images is labor-intensive and therefore, this organism size class is often neglected in ecosystem studies. Automated image analysis has been proposed as a possible approach to such analysis, but the heterogeneity of megafaunal communities poses a non-trivial challenge for such automated techniques. Here, the potential of a generalized object detection architecture, referred to as iSIS (intelligent Screening of underwater Image Sequences), for the quantification of a heterogenous group of megafauna taxa is investigated. The iSIS system is tuned for a particular image sequence (i.e. a transect) using a small subset of the images, in which megafauna taxa positions were previously marked by an expert. To investigate the potential of iSIS and compare its results with those obtained from human experts, a group of eight different taxa from one camera transect of seafloor images taken at the Arctic deep-sea observatory HAUSGARTEN is used. The results show that inter- and intra-observer agreements of human experts exhibit considerable variation between the species, with a similar degree of variation apparent in the automatically derived results obtained by iSIS. Whilst some taxa (e. g. Bathycrinus stalks, Kolga hyalina, small white sea anemone) were well detected by iSIS (i. e. overall Sensitivity: 87%, overall Positive Predictive Value: 67%), some taxa such as the small sea cucumber Elpidia heckeri remain challenging, for both human observers and iSIS

Semi-Automated Image Analysis for the Assessment of Megafaunal Densities at the Arctic Deep-Sea Observatory HAUSGARTEN

Megafauna play an important role in benthic ecosystem function and are sensitive indicators of environmental change. Non-invasive monitoring of benthic communities can be accomplished by seafloor imaging. However, manual quantification of megafauna in images is labor-intensive and therefore, this organism size class is often neglected in ecosystem studies. Automated image analysis has been proposed as a possible approach to such analysis, but the heterogeneity of megafaunal communities poses a non-trivial challenge for such automated techniques. Here, the potential of a generalized object detection architecture, referred to as iSIS (intelligent Screening of underwater Image Sequences), for the quantification of a heterogenous group of megafauna taxa is investigated. The iSIS system is tuned for a particular image sequence (i.e. a transect) using a small subset of the images, in which megafauna taxa positions were previously marked by an expert. To investigate the potential of iSIS and compare its results with those obtained from human experts, a group of eight different taxa from one camera transect of seafloor images taken at the Arctic deep-sea observatory HAUSGARTEN is used. The results show that inter- and intra-observer agreements of human experts exhibit considerable variation between the species, with a similar degree of variation apparent in the automatically derived results obtained by iSIS. Whilst some taxa (e. g. Bathycrinus stalks, Kolga hyalina, small white sea anemone) were well detected by iSIS (i. e. overall Sensitivity: 87%, overall Positive Predictive Value: 67%), some taxa such as the small sea cucumber Elpidia heckeri remain challenging, for both human observers and iSIS