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The growth in scientific production may threaten the capacity for the scientific community to handle the ever-increasing demand for peer review of scientific publications. There is little evidence regarding the sustainability of the peer-review system and how the scientific community copes with the burden it poses. We used mathematical modeling to estimate the overall quantitative annual demand for peer review and the supply in biomedical research. The modeling was informed by empirical data from various sources in the biomedical domain, including all articles indexed at MEDLINE. We found that for 2015, across a range of scenarios, the supply exceeded by 15% to 249% the demand for reviewers and reviews. However, 20% of the researchers performed 69% to 94% of the reviews. Among researchers actually contributing to peer review, 70% dedicated 1% or less of their research work-time to peer review while 5% dedicated 13% or more of it. An estimated 63.4 million hours were devoted to peer review in 2015, among which 18.9 million hours were provided by the top 5% contributing reviewers. Our results support that the system is sustainable in terms of volume but emphasizes a considerable imbalance in the distribution of the peer-review effort across the scientific community. Finally, various individual interactions between authors, editors and reviewers may reduce to some extent the number of reviewers who are available to editors at any point.

AbstractObjectiveTo examine the association between risk factor burdens—categorized as optimal, borderline, or elevated—and the lifetime risk of atrial fibrillation.DesignCommunity based cohort study.SettingLongitudinal data from the Framingham Heart Study.ParticipantsIndividuals free of atrial fibrillation at index ages 55, 65, and 75 years were assessed. Smoking, alcohol consumption, body mass index, blood pressure, diabetes, and history of heart failure or myocardial infarction were assessed as being optimal (that is, all risk factors were optimal), borderline (presence of borderline risk factors and absence of any elevated risk factor), or elevated (presence of at least one elevated risk factor) at index age.Main outcome measureLifetime risk of atrial fibrillation at index age up to 95 years, accounting for the competing risk of death.ResultsAt index age 55 years, the study sample comprised 5338 participants (2531 (47.4%) men). In this group, 247 (4.6%) had an optimal risk profile, 1415 (26.5%) had a borderline risk profile, and 3676 (68.9%) an elevated risk profile. The prevalence of elevated risk factors increased gradually when the index ages rose. For index age of 55 years, the lifetime risk of atrial fibrillation was 37.0% (95% confidence interval 34.3% to 39.6%). The lifetime risk of atrial fibrillation was 23.4% (12.8% to 34.5%) with an optimal risk profile, 33.4% (27.9% to 38.9%) with a borderline risk profile, and 38.4% (35.5% to 41.4%) with an elevated risk profile. Overall, participants with at least one elevated risk factor were associated with at least 37.8% lifetime risk of atrial fibrillation. The gradient in lifetime risk across risk factor burden was similar at index ages 65 and 75 years.ConclusionsRegardless of index ages at 55, 65, or 75 years, an optimal risk factor profile was associated with a lifetime risk of atrial fibrillation of about one in five; this risk rose to more than one in three in individuals with at least one elevated risk factor.

Background Prospective trial registration is a powerful tool to prevent reporting bias. We aimed to determine the extent to which published randomized controlled trials (RCTs) were registered and registered prospectively. Methods We searched MEDLINE and EMBASE from January 2005 to October 2017; we also screened all articles cited by or citing included and excluded studies, and the reference lists of related reviews. We included studies that examined published RCTs and evaluated their registration status, regardless of medical specialty or language. We excluded studies that assessed RCT registration status only through mention of registration in the published RCT, without searching registries or contacting the trial investigators. Two independent reviewers blinded to the other’s work performed the selection. Following PRISMA guidelines, two investigators independently extracted data, with discrepancies resolved by consensus. We calculated pooled proportions and 95% confidence intervals using random-effects models. Results We analyzed 40 studies examining 8773 RCTs across a wide range of clinical specialties. The pooled proportion of registered RCTs was 53% (95% confidence interval 44% to 58%), with considerable between-study heterogeneity. A subset of 24 studies reported data on prospective registration across 5529 RCTs. The pooled proportion of prospectively registered RCTs was 20% (95% confidence interval 15% to 25%). Subgroup analyses showed that registration was higher for industry-supported and larger RCTs. A meta-regression analysis across 19 studies (5144 RCTs) showed that the proportion of registered trials significantly increased over time, with a mean proportion increase of 27%, from 25 to 52%, between 2005 and 2015. Conclusions The prevalence of trial registration has increased over time, but only one in five published RCTs is prospectively registered, undermining the validity and integrity of biomedical research.

Mapping the international landscape of clinical trials may inform global health research governance, but no large-scale data are available. Industry or non-industry sponsorship may have a major influence in this mapping. We aimed to map the global landscape of industry- and non-industry-sponsored clinical trials and its evolution over time. We analyzed clinical trials initiated between 2006 and 2013 and registered in the WHO International Clinical Trials Registry Platform (ICTRP). We mapped single-country and international trials by World Bank's income groups and by sponsorship (industry- vs. non- industry), including its evolution over time from 2006 to 2012. We identified clusters of countries that collaborated significantly more than expected in industry- and non-industry-sponsored international trials. 119,679 clinical trials conducted in 177 countries were analysed. The median number of trials per million inhabitants in high-income countries was 100 times that in low-income countries (116.0 vs. 1.1). Industry sponsors were involved in three times more trials per million inhabitants than non-industry sponsors in high-income countries (75.0 vs. 24.5) and in ten times fewer trials in low- income countries (0.08 vs. 1.08). Among industry- and non-industry-sponsored trials, 30.3% and 3.2% were international, respectively. In the industry-sponsored network of collaboration, Eastern European and South American countries collaborated more than expected; in the non-industry-sponsored network, collaboration among Scandinavian countries was overrepresented. Industry-sponsored international trials became more inter-continental with time between 2006 and 2012 (from 54.8% to 67.3%) as compared with non-industry-sponsored trials (from 42.4% to 37.2%). Based on trials registered in the WHO ICTRP we documented a substantial gap between the globalization of industry- and non-industry-sponsored clinical research. Only 3% of academic trials but 30% of industry trials are international. The latter appeared to be conducted in preferentially selected countries.

Background Multiple treatments are frequently available for a given condition, and clinicians and patients need a comprehensive, up-to-date synthesis of evidence for all competing treatments. We aimed to quantify the waste of research related to the failure of systematic reviews to provide a complete and up-to-date evidence synthesis over time. Methods We performed a series of systematic overviews and networks of randomized trials assessing the gap between evidence covered by systematic reviews and available trials of second-line treatments for advanced non-small cell lung cancer. We searched the Cochrane Database of Systematic Reviews, Database of Abstracts of Reviews of Effects, MEDLINE, EMBASE, and other resources sequentially by year from 2009 to March 2, 2015. We sequentially compared the amount of evidence missing from systematic reviews to the randomized evidence available for inclusion each year. We constructed cumulative networks of randomized evidence over time and evaluated the proportion of trials, patients, treatments, and treatment comparisons not covered by systematic reviews on December 31 each year from 2009 to 2015. Results We identified 77 trials (28,636 patients) assessing 47 treatments with 54 comparisons and 29 systematic reviews (13 published after 2013). From 2009 to 2015, the evidence covered by existing systematic reviews was consistently incomplete: 45 % to 70 % of trials; 30 % to 58 % of patients; 40 % to 66 % of treatments; and 38 % to 71 % of comparisons were missing. In the cumulative networks of randomized evidence, 10 % to 17 % of treatment comparisons were partially covered by systematic reviews and 55 % to 85 % were partially or not covered. Conclusions We illustrate how systematic reviews of a given condition provide a fragmented, out-of-date panorama of the evidence for all treatments. This waste of research might be reduced by the development of live cumulative network meta-analyses.

Social isolation might be considered as a marker of poor health and higher mortality. The aim of our analysis was to assess the association of social network index (SNI) with incident AF and death. We selected participants aged ≥ 55 years without prevalent AF from the Framingham Heart Study. We evaluated the association between social isolation measured by the Berkman-Syme Social Network Index (SNI), incident AF, and mortality without diagnosed AF. We assessed the risk factor-adjusted associations between SNI (the sum of 4 components: marriage status, close friends/relatives, religious service attendance, social group participation), incident AF, and mortality without AF by using Fine-Gray competing risk regression models. We secondarily examined the outcome of all-cause mortality. We included 3454 participants (mean age 67 ± 10 years, 58% female). During 11.8 ± 5.2 mean years of follow-up, there were 686 incident AF cases and 965 mortality without AF events. Individuals with fewer connections had lower rates of incident AF ( P  = 0.04) but higher rates of mortality without AF ( P  = 0.03). Among SNI components, only social group participation was associated with higher incident AF (subdistribution hazards ratio [sHR] 1.35, 95% CI 1.16–1.57, P  = 0.0001). For mortality without AF, social group participation (sHR = 0.81, 95% CI 0.71–0.93, P  = 0.002) and regular religious service attendance sHR = 0.76, 95% CI 0.67–0.87, P  < 0.0001) were associated with lower risk of death. Social isolation was associated with a higher rate of mortality without diagnosed AF. In contrast to our hypothesis, we observed that poor social connectedness was associated with a lower rate of incident AF. This finding should be interpreted cautiously since there were very few participants in the lowest social connectedness group. Additionally, the seemingly protective effect of social isolation on AF incidence may be simply an artifact of the strong association between social isolation and increased mortality rate in combination with the large number of deaths as compared to AF events in our study. Further study is warranted.

Background Network meta-analysis (NMA), a generalization of conventional MA, allows for assessing the relative effectiveness of multiple interventions. Reporting bias is a major threat to the validity of MA and NMA. Numerous methods are available to assess the robustness of MA results to reporting bias. We aimed to extend such methods to NMA. Methods We introduced 2 adjustment models for Bayesian NMA. First, we extended a meta-regression model that allows the effect size to depend on its standard error. Second, we used a selection model that estimates the propensity of trial results being published and in which trials with lower propensity are weighted up in the NMA model. Both models rely on the assumption that biases are exchangeable across the network. We applied the models to 2 networks of placebo-controlled trials of 12 antidepressants, with 74 trials in the US Food and Drug Administration (FDA) database but only 51 with published results. NMA and adjustment models were used to estimate the effects of the 12 drugs relative to placebo, the 66 effect sizes for all possible pair-wise comparisons between drugs, probabilities of being the best drug and ranking of drugs. We compared the results from the 2 adjustment models applied to published data and NMAs of published data and NMAs of FDA data, considered as representing the totality of the data. Results Both adjustment models showed reduced estimated effects for the 12 drugs relative to the placebo as compared with NMA of published data. Pair-wise effect sizes between drugs, probabilities of being the best drug and ranking of drugs were modified. Estimated drug effects relative to the placebo from both adjustment models were corrected (i.e., similar to those from NMA of FDA data) for some drugs but not others, which resulted in differences in pair-wise effect sizes between drugs and ranking. Conclusions In this case study, adjustment models showed that NMA of published data was not robust to reporting bias and provided estimates closer to that of NMA of FDA data, although not optimal. The validity of such methods depends on the number of trials in the network and the assumption that conventional MAs in the network share a common mean bias mechanism.

Meta-epidemiological studies provide empirical evidence of trial characteristics associated with treatment effects. We aimed to evaluate methods used and characteristics associated with treatment effect in these studies. For this systematic review, we searched MEDLINE, Embase, Cochrane Methodology Register, Web of Science, and PROSPERO up to April 2015. We particularly assessed four key methodological components: constitution of the collection, clustering of trials within meta-analyses, heterogeneity assessment, and adjustment on meta-confounders. We also assessed trial characteristics evaluated and their association with treatment effect. We included 56 meta-epidemiological studies with data from 3,199 meta-analyses, 32 networks, and 21,468 trials. Thirty-two (58%) were published since 2010. Only 13 (23%) included all key methodological components. Overall, 58 trial characteristics were assessed. Allocation concealment and sequence generation were assessed in 22 (39%) and 17 (30%) meta-epidemiological studies, respectively, and trial size in 9 (16%). These characteristics were consistently associated with treatment effect estimates with larger effects in trials with inadequate sequence generation or allocation concealment or smaller trials. Key methodological components (e.g., constitution of the collection) were frequently missing. Concerning trial characteristics evaluated, there was consistent evidence that allocation concealment, sequence generation, and trial size were associated with treatment effect.

ObjectivesNarrative medicine (NM) incorporates stories into health sciences paradigms as fundamental aspects of the human experience. The aim of this systematic review is to answer the research question: how effective is the implementation and evaluation of NM programmes in academic medicine and health sciences? We documented objectives, content and evaluation outcomes of NM programming to provide recommendations for future narrative-based education.MethodsWe conducted a systematic review of literature published through 2019 using five major databases: PubMed, Embase, PsycINFO, ERIC and MedEdPORTAL. Eligible NM programming included textual analysis/close reading of published literature and creative/reflective writing. Qualifying participants comprised individuals from academic medicine and health sciences disciplines. We reviewed and categorised programme goals, content and evaluation activities to assess participant satisfaction and programme efficacy. Two members of the research team assessed the risk of bias, independently screening records via a two-round, iterative process to reach consensus on eligibility.ResultsOf 1569 original citations identified, we selected 55 unique programmes (described in 61 records). In all, 41 (75%) programmes reported a form of evaluation; evaluation methods lacked consistency. Twenty-two programmes used quantitative evaluation (13 well described), and 33 programmes used qualitative evaluation (27 well described). Well-described quantitative evaluations relied on 32 different measures (7 validated) and showed evidence of high participant satisfaction and pre-post improvement in competencies such as relationship-building, empathy, confidence/personal accomplishment, pedagogical skills and clinical skills. An average of 88.3% of participants agreed or strongly agreed that the programme had positive outcomes. Qualitative evaluation identified high participant satisfaction and improvement in competencies such as relationship-building, empathy, perspective-taking/reflection, resilience and burnout detection/mitigation, confidence/personal accomplishment, narrative competence, and ethical inquiry.ConclusionEvaluation suggests that NM programming leads to high participant satisfaction and positive outcomes across various competencies. We suggest best practices and innovative future directions for programme implementation and evaluation.

Coronary artery disease (CAD) is the leading cause of death among adults worldwide. Accurate risk stratification can support optimal lifetime prevention. Current methods lack the ability to incorporate new information throughout the life course or to combine innate genetic risk factors with acquired lifetime risk. We designed a general multistate model (MSGene) to estimate age-specific transitions across 10 cardiometabolic states, dependent on clinical covariates and a CAD polygenic risk score. This model is designed to handle longitudinal data over the lifetime to address this unmet need and support clinical decision-making. We analyze longitudinal data from 480,638 UK Biobank participants and compared predicted lifetime risk with the 30-year Framingham risk score. MSGene improves discrimination (C-index 0.71 vs 0.66), age of high-risk detection (C-index 0.73 vs 0.52), and overall prediction (RMSE 1.1% vs 10.9%), in held-out data. We also use MSGene to refine estimates of lifetime absolute risk reduction from statin initiation. Our findings underscore our multistate model’s potential public health value for accurate lifetime CAD risk estimation using clinical factors and increasingly available genetics toward earlier more effective prevention. Coronary artery disease is the leading cause of death among adults worldwide, however current risk stratification methods lack the ability to incorporate new information throughout the life-course or to combine innate genetic risk factors with acquired lifetime risk. Here the authors introduce a multistate model to address these limitations.