No abstract

The multicomponent workplace-based intervention was effective in reducing sitting time, prolonged sitting periods and body fat percentage, and in increasing the number of sit-to-stand transitions.

Background: Epilepsy in dogs is often difficult to medically control, resulting in premature death of dogs with epilepsy. However, the risks of premature death are not known. Hypothesis: Dogs with epilepsy have an increased risk of premature death as compared to a general population of dogs. Animals: Sixty-three dogs diagnosed with epilepsy between 1993 and 1996 were included in this study. Methods: A prospective longitudinal study of the population was performed from the diagnosis of epilepsy until the time of euthanasia, death, or a maximum of 12 years to investigate mortality and risk factors. Information about sex, onset, type, frequency, and control of seizures, remission of epilepsy, death, cause of death, and owner's perspective was collected and analyzed. Results: The median age at death of dogs was 7.0 years. The life span of dogs in which euthanasia or death was directly caused by their epileptic condition was significantly shorter as compared with epileptic dogs that were euthanized because of other causes (P 5 .001). The median number of years that a dog lived with epilepsy was 2.3 years. Females lived longer with epilepsy than males (P 5 .036). Seizure type (primary generalized versus focal seizures) was not significantly associated with survival time. The remission rate of epilepsy (spontaneous remission and remission with treatment) was 15%. Conclusion and Clinical Importance: The diagnosis of epilepsy implies an increased risk of premature death. The prognosis for dogs with epilepsy is dependent on a combination of veterinary expertise, therapeutic success, and the owner's motivation.

Long-term increased lithium exposure in drinking water may be associated with a lower incidence of dementia in a nonlinear way; however, confounding from other factors associated with municipality of residence cannot be excluded.

Background: Emergency medical dispatchers fail to identify approximately 25% of cases of out of hospital cardiac arrest, thus lose the opportunity to provide the caller instructions in cardiopulmonary resuscitation. We examined whether a machine learning framework could recognize out-of-hospital cardiac arrest from audio files of calls to the emergency medical dispatch center.Methods: For all incidents responded to by Emergency Medical Dispatch Center Copenhagen in 2014, the associated call was retrieved. A machine learning framework was trained to recognize cardiac arrest from the recorded calls. Sensitivity, specificity, and positive predictive value for recognizing out-of-hospital cardiac arrest were calculated. The performance of the machine learning framework was compared to the actual recognition and time-torecognition of cardiac arrest by medical dispatchers.Results: We examined 108,607 emergency calls, of which 918 (0.8%) were out-of-hospital cardiac arrest calls eligible for analysis. Compared with medical dispatchers, the machine learning framework had a significantly higher sensitivity (72.5% vs. 84.1%, p < 0.001) with lower specificity (98.8% vs. 97.3%, p < 0.001). The machine learning framework had a lower positive predictive value than dispatchers (20.9% vs. 33.0%, p < 0.001). Time-torecognition was significantly shorter for the machine learning framework compared to the dispatchers (median 44 seconds vs. 54 s, p < 0.001).Conclusions: A machine learning framework performed better than emergency medical dispatchers for identifying out-of-hospital cardiac arrest in emergency phone calls. Machine learning may play an important role as a decision support tool for emergency medical dispatchers.