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Non-standard errors
(2021)
In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants.
The most basic behavioural states of animals can be described as active or passive. However, while high-resolution observations of activity patterns can provide insights into the ecology of animal species, few methods are able to measure the activity of individuals of small taxa in their natural environment. We present a novel approach in which the automated VHF radio-tracking of small vertebrates fitted with lightweight transmitters (< 0.2 g) is used to distinguish between active and passive behavioural states.
A dataset containing > 3 million VHF signals was used to train and test a random forest model in the assignment of either active or passive behaviour to individuals from two forest-dwelling bat species (Myotis bechsteinii (n = 50) and Nyctalus leisleri (n = 20)). The applicability of the model to other taxonomic groups was demonstrated by recording and classifying the behaviour of a tagged bird and by simulating the effect of different types of vertebrate activity with the help of humans carrying transmitters. The random forest model successfully classified the activity states of bats as well as those of birds and humans, although the latter were not included in model training (F-score 0.96–0.98).
The utility of the model in tackling ecologically relevant questions was demonstrated in a study of the differences in the daily activity patterns of the two bat species. The analysis showed a pronounced bimodal activity distribution of N. leisleri over the course of the night while the night-time activity of M. bechsteinii was relatively constant. These results show that significant differences in the timing of species activity according to ecological preferences or seasonality can be distinguished using our method.
Our approach enables the assignment of VHF signal patterns to fundamental behavioural states with high precision and is applicable to different terrestrial and flying vertebrates. To encourage the broader use of our radio-tracking method, we provide the trained random forest models together with an R-package that includes all necessary data-processing functionalities. In combination with state-of-the-art open-source automated radio-tracking, this toolset can be used by the scientific community to investigate the activity patterns of small vertebrates with high temporal resolution, even in dense vegetation.
he most basic behavioural states of animals can be described as active or passive. While high-resolution observations of activity patterns can provide insights into the ecology of animal species, few methods are able to measure the activity of individuals of small taxa in their natural environment. We present a novel approach in which a combination of automatic radiotracking and machine learning is used to distinguish between active and passive behaviour in small vertebrates fitted with lightweight transmitters (<0.4 g).
We used a dataset containing >3 million signals from very-high-frequency (VHF) telemetry from two forest-dwelling bat species (Myotis bechsteinii [n = 52] and Nyctalus leisleri [n = 20]) to train and test a random forest model in assigning either active or passive behaviour to VHF-tagged individuals. The generalisability of the model was demonstrated by recording and classifying the behaviour of tagged birds and by simulating the effect of different activity levels with the help of humans carrying transmitters. The model successfully classified the activity states of bats as well as those of birds and humans, although the latter were not included in model training (F1 0.96–0.98).
We provide an ecological case-study demonstrating the potential of this automated monitoring tool. We used the trained models to compare differences in the daily activity patterns of two bat species. The analysis showed a pronounced bimodal activity distribution of N. leisleri over the course of the night while the night-time activity of M. bechsteinii was relatively constant. These results show that subtle differences in the timing of species' activity can be distinguished using our method.
Our approach can classify VHF-signal patterns into fundamental behavioural states with high precision and is applicable to different terrestrial and flying vertebrates. To encourage the broader use of our radiotracking method, we provide the trained random forest models together with an R package that includes all necessary data processing functionalities. In combination with state-of-the-art open-source automated radiotracking, this toolset can be used by the scientific community to investigate the activity patterns of small vertebrates with high temporal resolution, even in dense vegetation.
Background: Acute bleeding requires fast and targeted therapy. Therefore, knowledge of the patient's potential to form a clot is crucial. Point-of-care testing (POCT) provides fast and reliable information on coagulation. Structural circumstances, such as person-bound sample transport, can prolong the reporting of the results. The aim of the present study was to investigate the diagnostic quality and accuracy between POCT INR diagnostics and standard laboratory analysis (SLA) as well as the time advantage between a pneumatic tube and a personal-based transport system. Methods: Two groups of haemorrhagic patients (EG: emergency department; OG: delivery room; each n = 12) were examined in the context of bleeding emergencies using POCT and SLA. Samples were transported via a pneumatic tube system or by a personal transport service. Results: INR results between POCT and SLA showed a high and significant correlation (EG: p < 0.001; OG: p < 0.001). POCT results were reported significantly more quickly (EG: 1.1 vs. 39.6 min; OG: 2.0 vs. 75.0 min; p < 0.001) and required less time for analysis (EG: 0.3 vs. 24.0 min; OG: 0.5 vs. 45.0 min; p < 0.001) compared to SLA. The time for transportation with the pneumatic tube was significantly shorter (8.0 vs. 18.5 min; p < 0.001) than with the personal-based transport system. Conclusion: The results of the present study suggest that POCT may be a suitable method for the emergency diagnosis and may be used as prognostic diagnostic elements in haemotherapy algorithms to initiate targeted haemotherapy at an early point in time.
Allogeneic hematopoietic stem cell transplantation (allo-HSCT) offers potential cure to acute myeloid leukemia (AML) patients. However, infections with commensal bacteria are an important cause for non-relapse mortality (NRM). We have previously described the impact of multidrug-resistant organism (MDRO) colonization on the survival of allo-HSCT patients. In the aforementioned publication, according to consensus, we there did not consider the opportunistic gram-negative bacterium Stenotrophomonas maltophilia (S. maltophilia) to be an MDRO. Since rate of S. maltophilia colonization is increasing, and it is not known whether this poses a risk for allo-HSCT patients, we here analyzed here its effect on the previously described and now extended patient cohort. We report on 291 AML patients undergoing allo-HSCT. Twenty of 291 patients (6.9%) were colonized with S. maltophilia. Colonized patients did not differ from non-colonized patients with respect to their age, remission status before allo-HSCT, donor type and HSCT-comorbidity index. S. maltophilia colonized patients had a worse overall survival (OS) from 6 months up to 60 months (85% vs. 88.1% and 24.7% vs. 59.7%; p = 0.007) due to a higher NRM after allo-HSCT (6 months: 15% vs. 4.8% and 60 months: 40.1% vs. 16.2% p = 0.003). The main cause of mortality in colonized patients was infection (46.2% of all deaths) and in non-colonized patients relapse (58.8% of all deaths). 5/20 colonized patients developed an invasive infection with S. maltophilia. The worse OS after allo-HSCT due to higher infection related mortality might implicate the screening of allo-HSCT patients for S. maltophilia and a closer observation of colonized patients as outpatients.
Objective: Nationwide data on the epidemiology, treatment characteristics, and long-term outcome of severe traumatic brain injury (TBI) in Germany is not yet existing. Neurosurgeons from the German Neurosurgery Society (DGNC) and traumatologists from the German Trauma Society (DGU), therefore, joined forces in 2016 to conceptualize a TBI module for the well-established Trauma Register of the DGU (TR-DGU). Here, we report how this “German National TBI registry (GNTR)” has been developed, implemented, and tested in a recently completed pilot period.
Methods: The conception and implementation process of the GNTR from August 2016 to February 2019 is described, and results of its 23-months long pilot period from February 2019 to December 2020 are presented. For the pilot period, TBI patients were prospectively enrolled at nine neurosurgical and traumatological hospitals across Germany. Inclusion criteria were treatment on the ICU ≥ 24h, or an ISS score ≥ 16. A variety of clinical, imaging, and laboratory parameters were collected, and the GOSE score was used to assess the outcome at discharge and 6- and 12 months follow-up.
Results: Details on the structure and dataset of the GNTR as well as milestones and pitfalls during its conception and implementation, are outlined. During the pilot period, a total of 264 TBI patients were enrolled. Their demographic characteristics, clinical, imaging, and radiological findings, and their early mortality and functional outcome are described. Furthermore, factors associated with an unfavorable outcome (GOSE 1-4) are assessed using uni- and multivariate regression analyses. Finally, problems and future directions of the GNTR are discussed.
Conclusion: The pilot period of the GNTR offers a first glance at the current epidemiology and treatment characteristics of TBI patients in Germany. More importantly, they show how a national TBI registry yielding high-quality prospective data can be developed, implemented, and tested within four years