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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.

IMPORTANCEEmergency medical dispatchers fail to identify approximately 25% of cases of out-ofhospital cardiac arrest (OHCA), resulting in lost opportunities to save lives by initiating cardiopulmonary resuscitation. OBJECTIVE To examine how a machine learning model trained to identify OHCA and alert dispatchers during emergency calls affected OHCA recognition and response. DESIGN, SETTING, AND PARTICIPANTS This double-masked, 2-group, randomized clinical trial analyzed all calls to emergency number 112 (equivalent to 911) in Denmark. Calls were processed by a machine learning model using speech recognition software. The machine learning model assessed ongoing calls, and calls in which the model identified OHCA were randomized. The trial was

ObjectiveTo analyse injuries related to manual and electric scooter use from January 2016 up to and including July 2019.SettingElectric scooter rental services were launched in Denmark in January 2019. The services were provided by private companies. Although rules for handling and riding scooters have been established, no reports either before or after introduction of electric scooters anticipated the full extent of use, and injuries to riders and pedestrians.ParticipantsAll patient records mentioning manual or electric scooters. Records were reviewed, and data were stratified according to two groups: manual and electric scooters.InterventionsA predefined survey was completed in all cases where ‘scooter’ was present. This contained variables such as type of scooter, type of participant, mechanism of injury, acuity, intoxication, referral to treatment facility.Outcome measuresAmong incidents involving scooters, summary statistics on continuous and categorical variables of interest were reported.Results468 scooter-related injuries were recorded. We found that manual scooter riders were more likely to be children under the age of 15; fall alone—involving no other party; sustain contusions, sprains and lacerations; and bruise either their fingers or toes. Riders of electric scooters were likely to be 18–25 years, sustain facial bruising and lacerations requiring sutures, and be under the influence of alcohol or drugs. Non-riders of electric scooters were mostly elderly people who tripped over scooters, consequently sustaining moderate to severe injuries.ConclusionThere were two different types of population sustaining injuries from manual and electric scooters, respectively. The proportion of non-riders injured by electric scooters were surprisingly large (17%), and while electric scooters are here to stay, several apparently preventable injuries occur as a result of reckless driving and discarded electric scooters. Current rules for usage might not prevent unnecessary accidents and secure traffic safety and the lives of older individuals.

Total edentulousness can lead to chewing problems as well as to feelings of insecurity and inferiority and considerable psycho-social problems. For many people a conventional removable denture is unsatisfactory. A new method - osseointegration - involves a titanium screw being operated into the jawbone and the attachment of a fixed bridge. In a controlled study, 26 patients were examined pre- and postoperatively 3 months and then 2 years after the insertion of a jawbone-anchored bridge. The majority of them state that there has been a significant improvement in their lives, that they have regained confidence in themselves, and that, in contrast to a conventional denture, they accept the fixed bridge as part of their body. More attention should be focused on psychological reactions to total edentulousness. Individuals who cannot be rehabilitated by means of conventional prosthetic procedures should be given the opportunity of having a jawbone-anchored bridge inserted. Such treatment means an odontological and psycho-social restitutio ad integrum.

A questionnaire was sent to all 189 edentulous patients with denture adaptation problems who were treated with fixed prostheses on osseointegrated oral implants during the period 1965-1978. One hundred and fifty-two patients (80%) responded. Practically all had adapted well to the prostheses and were most satisfied with the rehabilitated oral function, including chewing ability. Four out of five patients regarded the bridge as part of their own body instead of a foreign object, and 90% would not hesitate to have the treatment performed again, if necessary. Parallel with the improved oral function the patients reported a definite reduction of psychosocial problems associated with their previous oral invalidity, and increased security and self-esteem.

Background: In emergencies, such as the COVID-19 pandemic, there is an increased need for contact with emergency medical services (EMS), and call volume might surpass capacity. The Copenhagen EMS operates two telephone line the 1-1-2 emergency number and the 1813 medical helpline. A separate coronavirus support track was implemented on the 1813 medical helpline and a web-based self-triage (web triage) system was created to reduce non-emergency call volume. The aim of this paper is to present call volume and the two measures implemented to handle the increased call volume to the Copenhagen EMS. Methods: This is a cross sectional observational study. Call volume and queue time is presented in the first month of the COVID-19 pandemic (27th

Objective To assess the patterns in psychiatric admissions, referrals, and suicidal behavior before and during the COVID‐19 pandemic. Methods This study utilized health records from hospitals and Emergency Medical Services (EMS) covering 46% of the Danish population (n = 2,693,924). In a time‐trend study, we compared the number of psychiatric in‐patients, referrals to mental health services and suicidal behavior in years prior to the COVID‐19 pandemic to levels during the first lockdown (March 11 – May 17, 2020), inter‐lockdown period (May 18 – December 15, 2020), and second lockdown (December 16, 2020 – February 28, 2021). Results During the pandemic, the rate of psychiatric in‐patients declined compared to pre‐pandemic levels (RR = 0.95, 95% CI = 0.94 – 0.96, p < 0.01), with the largest decrease of 19% observed three weeks into the first lockdown. Referrals to mental health services were not significantly different (RR = 1.01, 95% CI = 0.92 – 1.10, p = 0.91) during the pandemic; neither was suicidal behavior among hospital contacts (RR = 1.04, 95% CI = 0.94 – 1.14, p = 0.48) nor EMS contacts (RR = 1.08, 95% CI = 1.00 – 1.18, p = 0.06). Similar trends were observed across nearly all age groups, sexes, and types of mental disorders examined. In the age group <18, an increase in the rate of psychiatric in‐patients (RR = 1.11, 95% CI = 1.07 – 1.15, p < 0.01) was observed during the pandemic; however, this did not exceed the pre‐pandemic, upwards trend in psychiatric hospitalizations in the age group <18 (p = 0.78). Conclusion The COVID‐19 pandemic has been associated with a decrease in psychiatric hospitalizations, while no significant change was observed in referrals to mental health services and suicidal behavior. Psychiatric hospitalizations among children and adolescents increased during the pandemic; however, this appears to be a continuation of a pre‐pandemic trend.