Nordic Life Science Pipeline (Canada)

Abstract. Snow avalanches are a serious threat to traffic in the northern Gaspésie region. In this study, we look at the development of different forecasting models using machine learning (ML), based on snow avalanche events recorded by Québec's Ministry of Transportation, meteorological data from the Cap-Madeleine station and Environment Canada weather forecast data. The models were trained and tested on Train and Test datasets with meteorological and weather forecasts recorded at the Meteorological Station. Unsupervised learning models were compared to expert models where only 4 variables were selected with avalanche expertise in mind, yielding similar results in prediction. The ML models were then tested in a realistic forecasting context over the year 2019 with weather data from a forecasting station (Hindcast) and with weather forecast data over 24 and 48 h. The LR and RF models show that model performance can match or exceed that of current forecasting tools, enhancing hazard anticipation while maintaining a user-friendly framework suitable for real-time application. In conclusion, recommendations on forecast-based operational procedures are proposed.

In todays society, domestic violence has stood out as a major social concern, given its profound influence on public harmony and stability. Severe physical and psychological damage is a common plight for victims, and under certain circumstances, they may be forced to resort to extreme means to break free from the abusive situation. However, in judicial practice, these defensive measures are often deemed intentional homicide or assault, failing to gain reasonable recognition as legitimate self-defense. Such a scenario leaves victims trapped in an awkward legal dilemma: enduring the abuse passively means continuing their suffering, while resisting actively brings heavy legal risks. Chinas Anti-Domestic Violence Law has established a legal system to handle abuse, yet it offers no explicit criteria for the application of self-defense in domestic violence cases. As such, developing legal mechanisms that well balance jurisprudential principles and humanitarian considerations has become an urgent mission.

Abstract Dry‐snow slab avalanches are the most fatal type of avalanches, beginning with the failure of a weak snow layer below cohesive slabs. This failure can propagate within the weak layer, causing the overlying slab to fracture and slide. Avalanche forecasters are interested in evaluating crack propagation propensity and potential avalanche sizes. This study tests the hypothesis that two factors may stop dynamic crack propagation: snowpack heterogeneity and terrain variations. We develop a depth‐averaged Material Point Method, which combines MPM with shallow water assumptions for efficient elastic‐brittle modeling of avalanche release. We analyze two scenarios: pure‐elastic and brittle slabs. In the pure‐elastic case, we observe a significant decrease in slab tensile stress with increasing crack speed and provide an analytical formulation for this phenomenon. We evaluate the impacts of weak layer heterogeneity and fracture energy on crack stopping. In the brittle scenario, we explore the interaction between weak layer heterogeneity and slab fracture, quantifying their combined effects on crack arrest. Our results reveal a scaling law that relates crack arrest distance to dimensionless numbers indicative of weak layer and slab strength. The model is applied in case studies to predict release sizes based on field data, and also on synthetic 3D topographies, enhancing the understanding of factors influencing avalanche size and aiding future mitigation strategies.

<strong class="journal-contentHeaderColor">Abstract.</strong> Snow avalanches are a serious threat to traffic in the northern Gasp&eacute;sie region. In this study, we look at the development of different forecasting models using machine learning (ML), based on snow avalanche events recorded by Quebec's Ministry of Transportation (MTMQ), meteorological data from the Cap-Madeleine station and Environment Canada weather forecast data. The models were trained and tested on <em>Train</em> and <em>Test</em> datasets with meteorological and weather forecasts recorded at the Meteorological Station. Unsupervised learning models were compared to expert models where only 4 variables were selected with avalanche expertise in mind, yielding similar results in prediction. The ML models were then tested in a realistic forecasting context over the year 2019 with weather data from a forecasting station (Hindcast) and with forecast data over 24 h and 48 h (GEMLAM 24 h). The LR and RF models show that model performance can match or exceed that of current forecasting tools, enhancing hazard anticipation while maintaining a user-friendly framework suitable for real-time application. In conclusion, recommendations on forecast-based operational procedures are proposed.

IntroductionDepressive disorders often require specialised psychiatric services. Timely, appropriate medication initiation and/or change plays a crucial role in improving patient (pt) outcomes (Kraus et al. Transl Psychiatry 2019;9 127).ObjectivesDescribe the type of, and time to, medication changes within 12 months of the initial assessment of pts with depression recorded by specialised psychiatric care (SPC).MethodsThis cohort study leveraged Finnish pt data from 19 registries from 2014–2020. Adult pts with a depression diagnosis recorded by SPC in the Helsinki and Uusimaa region in 2015 (with no depression diagnosis given by SPC within the previous year) were included. All treatments were recorded as monotherapy or combination/augmentation therapy. The Kaplan-Meier method was used to analyse time to treatment change (TTC).Results9305 pts were included; baseline characteristics are reported (Table 1). There was no change to the baseline treatment status in 39.7% of pts (Table 2). The most common change was from no medication to monotherapy (2138 pts [45.6% of those with no treatment before]). 2202 (23.7%) pts remained untreated throughout the study. Median (95% confidence interval) TTC following the initial assessment by SPC was 53 (50–56) days (Figure 1).Image 1:Image 2:Image 3:ConclusionsAround 40% of pts referred to SPC had no prior pharmacotherapy. Monotherapy was the most common treatment provided. Almost 40% of pts had no change in their baseline treatment over 12 months, highlighting the need for further research to optimise care.Disclosure of InterestNone Declared

Abstract Background and purpose Esophageal and gastric cancer patients, with poor prognoses and complex symptom burdens, require comprehensive end-of-life care. This study evaluated the impact of specialist palliative care (SPC) on end-of-life healthcare utilization. Material and methods We retrospectively analyzed nationwide healthcare utilization data for all adults who died of esophageal or gastric cancer in Finland in 2019, using National Health and Social Care Registers. Patients were compared based on timing of first SPC contact: early (&gt;30 days before death) or no/late (≤30 days). Results The cohort included 732 patients (median age 72 years), with 233 (32%) having SPC contact, including 156 (21%) with early SPC. Most patients (79%) had late/no SPC. The median time for the first SPC contact was 120 days before death for early SPC group, and 12 days for no/late group, p&lt;0.001. Early SPC group, compared to no/late SPC, had less emergency department contacts (44% vs. 60%, p&lt;0.001) and secondary care hospitalizations (32% vs. 61%, p&lt;0.001) in the last month. Early SPC increased access to hospital-at-home (56% vs. 6%, p&lt;0.001) and SPC wards (19% vs. 4%, p&lt;0.001). Patients with early SPC died more often in SPC wards (19% vs. 4%, p&lt;0.001). Overall, 122 (17%) received hospital-at-home care, and were more likely to die at home (19% vs. 11%, p=0.011) or in SPC wards (15% vs. 5%, p&lt;0.001). Conclusions Few patients with advanced esophageal or gastric cancer receive SPC. Early SPC reduces healthcare utilization and improves access to SPC services, highlighting the importance of timely SPC initiation.

BackgroundMany patients with chronic heart failure (HF) experience a reduced health status, leading to readmission after hospitalization despite receiving conventional care. Telemonitoring approaches aim to improve the early detection of HF decompensations and prevent readmissions. However, knowledge about the impact of telemonitoring on preventing readmissions and related costs remains scarce.ObjectiveThis study assessed the effectiveness of adding a telemonitoring solution to the standard of care (SOC) for the prevention of hospitalization and related costs in patients with HF in Finland.MethodsWe performed a nonrandomized pre-post telemonitoring study to estimate health care costs and resource use during 6 months on SOC followed by 6 months on SOC with a novel telemonitoring solution. The telemonitoring solution consisted of a digital platform for patient-reported symptoms and daily weight and blood pressure measurements, automatically generated alerts triggering phone calls with secondary care nurses, and rapid response to alerts by treating physicians. Telemonitoring solution data were linked to patient register data on primary care, secondary care, and hospitalization. The patient register of the Southern Savonia Social and Health Care Authority (Essote) was used. Eligible patients had at least 1 hospital admission within the last 12 months and self-reported New York Heart Association class II-IV from the central hospital in the Southern Savonia region.ResultsOut of 50 recruited patients with HF, 43 completed the study and were included in the analysis. The hospitalization-related cost decreased (49%; P=.03) from €2189 (95% CI €1384-€2994; a currency exchange rate of EUR €1=US $1.10589 is applicable) during SOC to €1114 (95% CI €425-€1803) during telemonitoring. The number of patients with at least 1 hospitalization due to HF was reduced by 70% (P=.002) from 20 (47%) out of 43patients during SOC to 6 (14%) out of 43 patients in telemonitoring. The estimated mean total health care cost per patient was €3124 (95% CI €2212-€4036) during SOC and €2104 (95% CI €1313-€2895) during telemonitoring, resulting in a 33% reduction (P=.07) in costs with telemonitoring.ConclusionsThe results suggest that the telemonitoring solution can reduce hospital-related costs for patients with HF with a recent hospital admission.

BackgroundHealthcare costs are rising, and a substantial proportion of medical care is of little value. De-implementation of low-value practices is important for improving overall health outcomes and reducing costs. We aimed to identify and synthesize randomized controlled trials (RCTs) on de-implementation interventions and to provide guidance to improve future research.MethodsMEDLINE and Scopus up to May 24, 2021, for individual and cluster RCTs comparing de-implementation interventions to usual care, another intervention, or placebo. We applied independent duplicate assessment of eligibility, study characteristics, outcomes, intervention categories, implementation theories, and risk of bias.ResultsOf the 227 eligible trials, 145 (64%) were cluster randomized trials (median 24 clusters; median follow-up time 305 days), and 82 (36%) were individually randomized trials (median follow-up time 274 days). Of the trials, 118 (52%) were published after 2010, 149 (66%) were conducted in a primary care setting, 163 (72%) aimed to reduce the use of drug treatment, 194 (85%) measured the total volume of care, and 64 (28%) low-value care use as outcomes. Of the trials, 48 (21%) described a theoretical basis for the intervention, and 40 (18%) had the study tailored by context-specific factors. Of the de-implementation interventions, 193 (85%) were targeted at physicians, 115 (51%) tested educational sessions, and 152 (67%) multicomponent interventions. Missing data led to high risk of bias in 137 (60%) trials, followed by baseline imbalances in 99 (44%), and deficiencies in allocation concealment in 56 (25%).ConclusionsDe-implementation trials were mainly conducted in primary care and typically aimed to reduce low-value drug treatments. Limitations of current de-implementation research may have led to unreliable effect estimates and decreased clinical applicability of studied de-implementation strategies. We identified potential research gaps, including de-implementation in secondary and tertiary care settings, and interventions targeted at other than physicians. Future trials could be improved by favoring simpler intervention designs, better control of potential confounders, larger number of clusters in cluster trials, considering context-specific factors when planning the intervention (tailoring), and using a theoretical basis in intervention design.RegistrationOSF Open Science Framework hk4b2