BackgroundA standard or consensus definition of a systematic review does not exist. Therefore, if there is no definition about a systematic review in secondary studies that analyse them or the definition is too broad, inappropriate studies might be included in such evidence synthesis. The aim of this study was to analyse the definition of a systematic review (SR) in health care literature, elements of the definitions that are used and to propose a starting point for an explicit and non-ambiguous SR definition.MethodsWe included overviews of systematic reviews (OSRs), meta-epidemiological studies and epidemiology textbooks. We extracted the definitions of SRs, as well as the inclusion and exclusion criteria that could indicate which definition of a SR the authors used. We extracted individual elements of SR definitions, categorised and quantified them.ResultsAmong the 535 analysed sources of information, 188 (35%) provided a definition of a SR. The most commonly used reference points for the definitions of SRs were Cochrane and the PRISMA statement. We found 188 different elements of SR definitions and divided them into 14 categories. The highest number of SR definition elements was found in categories related to searching (N = 51), analysis/synthesis (N = 23), overall methods (N = 22), quality/bias/appraisal/validity (N = 22) and aim/question (N = 13). The same five categories were also the most commonly used combination of categories in the SR definitions.ConclusionCurrently used definitions of SRs are vague and ambiguous, often using terms such as clear, explicit and systematic, without further elaboration. In this manuscript we propose a more specific definition of a systematic review, with the ultimate aim of motivating the research community to establish a clear and unambiguous definition of this type of research.

Background Second medical opinions can give patients confidence when choosing among treatment options and help them understand their diagnosis. Health insurers in several countries, including Germany, offer formal second opinion programs (SecOPs). We systematically collected and analyzed information on German health insurers’ approach to SecOPs, how the SecOPs are structured, and to what extent they are evaluated. Methods In April 2019, we sent a questionnaire by post to all German statutory (n = 109) and private health insurers (n = 52). In September 2019, we contacted the nonresponders by email. The results were analyzed descriptively. They are presented overall and grouped by type of insurance (statutory/private health insurer). Results Thirty one of One hundred sixty one health insurers (response rate 19%) agreed to participate. The participating insurers covered approximately 40% of the statutory and 34% of the private health insured people. A total of 44 SecOPs were identified with a median of 1 SecOP (interquartile range (IQR) 1–2) offered by a health insurer. SecOPs were in place mainly for orthopedic (21/28 insurers with SecOPs; 75%) and oncologic indications (20/28; 71%). Indications were chosen principally based on their potential impact on a patient (22/28; 79%). The key qualification criterion for second opinion providers was their expertise (30/44 SecOPs; 68%). Second opinions were usually provided based on submitted documents only (21/44; 48%) or on direct contact between a patient and a doctor (20/44; 45%). They were delivered after a median of 9 days (IQR 5–15). A median of 31 (IQR 7–85) insured persons per year used SecOPs. Only 12 of 44 SecOPs were confirmed to have conducted a formal evaluation process (27%) or, if not, plan such a process in the future (10/22; 45%). Conclusion Health insurers’ SecOPs focus on orthopedic and oncologic indications and are based on submitted documents or on direct patient-physician contact. The formal evaluation of SecOPs needs to be expanded and the results should be published. This can allow the evaluation of the impact of SecOPs on insured persons’ health status and satisfaction, as well as on the number of interventions performed. Our results should be interpreted with caution due to the low participation rate.

Background: The rate of caesarean sections (CS) has increased in the last decades to about 30% of births in high income countries. Many CSs are electively planned without an urgent medical reason for mother or child. An early CS though may harm the newborn. Our aim was to evaluate the gestational time point after the 37 + 0 week of gestation (WG) (after prematurity = term) of performing an elective CS with the lowest morbidity for mother and child by assessing the time course from 37 + 0 to 42+ 6 WG. Methods: We performed a systematic literature search in MEDLINE, EMBASE, CENTRAL and CINAHL in November 2018. We included studies that compared different time points of elective CS at term no matter the reason for elective CS. Our primary outcomes were the rate of admissions to the neonatal intensive care unit (NICU), neonatal death and maternal death in early versus late term elective CS. Various binary and dose response random effects meta-analyses were performed. Results: We identified 35 studies including 982,749 women. Except one randomised controlled trial, all studies were cohort studies. We performed a linear time-response meta-analysis on the primary outcome NICU admission on 14 studies resulting in a decrease of the relative risk (RR) to 0.63 (95% CI 0.56, 0.71) from 37 + 0 to 39 + 6 WG. RR for neonatal death showed a decrease to 39 + (0-6) WG (RR 0.59 95% CI 0.43 to 0.83) and increase from then on (RR 2.09 95% CI 1.18 to 3.70) assuming a U-shape course and using a cubic spline model for meta-analysis of four studies. We only identified one study analyzing maternal death resulting in RR of 0.38 (95% CI 0.04 to 3.40) for 37 + 0 + 38 + 6 WG versus ≥39 + 0 WG. Conclusion: Our systematic review showed that elective CS (primary and repeated) before the 39 + 0 WG lead to more NICU admissions and neonatal deaths, although death is rare and increases again after 39 + 6 WG. We did not find enough evidence on maternal outcomes. There is a need for more research, considering maternal outcomes to provide a balanced decision between neonatal and maternal health.

Background We assessed predictive factors of patients with fractures of the lower extremities caused by trauma. We examined which factors are associated with an increased risk of failure. Furthermore, the predictive factors were set into context with other long-term outcomes, concrete pain and physical functioning. Methods We performed a prospective cohort study at a single level I trauma center. We enrolled patients with traumatic fractures of the lower extremities treated with internal fixation from April 2017 to July 2018. We evaluated the following predictive factors: age, gender, diabetes, smoking status, obesity, open fractures and peripheral arterial diseases. The primary outcome was time to failure (nonunion, implant failure or reposition). Secondary outcomes were pain and physical functioning measured 6 months after initial surgery. For the analysis of the primary outcome, we used a stratified (according fracture location) Cox proportional hazard regression model. Results We included 204 patients. Overall, we observed failure in 33 patients (16.2 %). Most of the failures occurred within the first 3 months. Obesity and open fractures were associated with an increased risk of failure and decreased physical functioning. None of the predictors showed an association with pain. Age, female gender and smoking of more than ≥ 10 package years increased failure risk numerically but statistical uncertainty was high. Conclusions We found that obesity and open fractures were strongly associated with an increased risk of failure. These predictors seem promising candidates to be included in a risk prediction model and can be considered as a good start for clinical decision making across different types of fractures at the lower limbs. However, large heterogeneity regarding the other analyzed predictors suggests that “simple” models might not be adequate for a precise personalized risk estimation and that computer-based models incorporating a variety of detailed information (e.g. pattern of injury, x-ray and clinical data) and their interrelation may be required to significantly increase prediction precision. Trial registration NCT03091114.

BackgroundWe assessed predictive factors of patients with fractures of the lower extremities caused by trauma. We examined which factors might increase failure rates. Furthermore, the predictive factors were set into context with other long-term outcomes, concrete pain and physical functioning.MethodsWe performed a prospective cohort study at a single level I trauma center. We enrolled patients with traumatic fractures of the lower extremities treated with internal fixation from April 2017 to July 2018. We evaluated the following predictive factors: age, gender, diabetes, smoking status, obesity, open fractures and peripheral arterial diseases. The primary outcome was time to failure (nonunion, implant failure or reposition), secondary outcomes were pain and physical functioning measured at follow up 6 months after initial surgery. For the analysis of the primary outcome we used a multivariate stratified (according fracture location) Cox proportional hazard regression model.Results We included 204 patients. Overall, we observed a failure in 33 patients (16.2%). Most of the failures occurred within the first 3 months. Obesity and open fractures increased the risk of failure and decreased physical functioning. None of the predictors had an impact on pain. Age, female gender and smoking of more than ≥ 10 package years increased failure risk numerically but statistical uncertainty was high.Conclusion We found that obesity and open fractures strongly increased the risk of failure. These seem promising candidates to be included in a risk prediction model and can be considered as a good start for clinical decision making across different types of fractures in the lower limb. However, large heterogeneity in the other analyzed factors suggest that for a precise personalized risk estimation, computer-based models incorporating a variety of detailed information (e.g. pattern of injury, x-ray and clinical data) and their interrelation might be needed to increase precision of prediction significantly.Trial registrationNCT03091114