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Even today, patients with schizophrenia often have an unfavorable outcome. Negative symptoms and cognitive deficits are common features in many patients and prevent recovery. In recent years, aerobic endurance training has emerged as a therapeutic approach with positive effects on several domains of patients’ health. However, appropriately sized, multicenter randomized controlled trials that would allow better generalization of results are lacking. The exercise study presented here is a multicenter, rater-blind, two-armed, parallel-group randomized clinical trial in patients with clinically stable schizophrenia being conducted at five German tertiary hospitals. The intervention group performs aerobic endurance training on bicycle ergometers three times per week for 40–50 min/session (depending on the intervention week) for a total of 26 weeks, and the control group performs balance and tone training for the same amount of time. Participants are subsequently followed up for 26 weeks. The primary endpoint is all-cause discontinuation; secondary endpoints include psychopathology, cognition, daily functioning, cardiovascular risk factors, and explorative biological measures regarding the underlying mechanisms of exercise. A total of 180 patients will be randomized. With currently 162 randomized participants, our study is the largest trial to date to investigate endurance training in patients with schizophrenia. We hypothesize that aerobic endurance training has beneficial effects on patients’ mental and physical health, leading to lower treatment discontinuation rates and improving disease outcomes. The study results will provide a basis for recommending exercise interventions as an add-on therapy in patients with schizophrenia.The study is registered in the International Clinical Trials Database (ClinicalTrials.gov identifier [NCT number]: NCT03466112) and in the German Clinical Trials Register (DRKS-ID: DRKS00009804).

Backgrounds Syntheses of non-randomized studies of interventions (NRSIs) and randomized controlled trials (RCTs) are increasingly used in decision-making. This study aimed to summarize when NRSIs are included in evidence syntheses of RCTs, with a particular focus on the methodological issues associated with combining NRSIs and RCTs. Methods We searched PubMed to identify clinical systematic reviews published between 9 December 2017 and 9 December 2022, randomly sampling reviews in a 1:1 ratio of Core and non-Core clinical journals. We included systematic reviews with RCTs and NRSIs for the same clinical question. Clinical scenarios for considering the inclusion of NRSIs in eligible studies were classified. We extracted the methodological characteristics of the included studies, assessed the concordance of estimates between RCTs and NRSIs, calculated the ratio of the relative effect estimate from NRSIs to that from RCTs, and evaluated the impact on the estimates of pooled estimates when NRSIs are included. Results Two hundred twenty systematic reviews were included in the analysis. The clinical scenarios for including NRSIs were grouped into four main justifications: adverse outcomes ( n  = 140, 63.6%), long-term outcomes ( n  = 36, 16.4%), the applicability of RCT results to broader populations ( n  = 11, 5.0%), and other ( n  = 33, 15.0%). When conducting a meta-analysis, none of these reviews assessed the compatibility of the different types of evidence prior, 203 (92.3%) combined estimates from RCTs and NRSIs in the same meta-analysis. Of the 203 studies, 169 (76.8%) used crude estimates of NRSIs, and 28 (13.8%) combined RCTs and multiple types of NRSIs. Seventy-seven studies (35.5%) showed “qualitative disagree” between estimates from RCTs and NRSIs, and 101 studies (46.5%) found “important difference”. The integration of NRSIs changed the qualitative direction of estimates from RCTs in 72 out of 200 studies (36.0%). Conclusions Systematic reviews typically include NRSIs in the context of assessing adverse or long-term outcomes. The inclusion of NRSIs in a meta-analysis of RCTs has a substantial impact on effect estimates, but discrepancies between RCTs and NRSIs are often ignored. Our proposed recommendations will help researchers to consider carefully when and how to synthesis evidence from RCTs and NRSIs.

Controversial and misleading interpretation of data from randomized trials is common. How to avoid misleading interpretation has received little attention. Herein, we describe two applications of an approach that involves blinded interpretation of the results by study investigators. The approach involves developing two interpretations of the results on the basis of a blinded review of the primary outcome data (experimental treatment A compared with control treatment B). One interpretation assumes that A is the experimental intervention and another assumes that A is the control. After agreeing that there will be no further changes, the investigators record their decisions and sign the resulting document. The randomization code is then broken, the correct interpretation chosen, and the manuscript finalized. Review of the document by an external authority before finalization can provide another safeguard against interpretation bias. We found the blinded preparation of a summary of data interpretation described in this article practical, efficient, and useful. Blinded data interpretation may decrease the frequency of misleading data interpretation. Widespread adoption of blinded data interpretation would be greatly facilitated were it added to the minimum set of recommendations outlining proper conduct of randomized controlled trials (eg, the Consolidated Standards of Reporting Trials statement).

Purpose In the literature, the propriety of the meta-analytic treatment-effect produced by combining randomized controlled trials (RCT) and non-randomized studies (NRS) is questioned, given the inherent confounding in NRS that may bias the meta-analysis. The current study compared an implicitly principled pooled Bayesian meta-analytic treatment-effect with that of frequentist pooling of RCT and NRS to determine how well each approach handled the NRS bias. Materials & methods Binary outcome Critical-Care meta-analyses, reflecting the importance of such outcomes in Critical-Care practice, combining RCT and NRS were identified electronically. Bayesian pooled treatment-effect and 95% credible-intervals (BCrI), posterior model probabilities indicating model plausibility and Bayes-factors (BF) were estimated using an informative heavy-tailed heterogeneity prior (half-Cauchy). Preference for pooling of RCT and NRS was indicated for Bayes-factors > 3 or < 0.333 for the converse. All pooled frequentist treatment-effects and 95% confidence intervals (FCI) were re-estimated using the popular DerSimonian-Laird (DSL) random effects model. Results Fifty meta-analyses were identified (2009–2021), reporting pooled estimates in 44; 29 were pharmaceutical-therapeutic and 21 were non-pharmaceutical therapeutic. Re-computed pooled DSL FCI excluded the null (OR or RR = 1) in 86% (43/50). In 18 meta-analyses there was an agreement between FCI and BCrI in excluding the null. In 23 meta-analyses where FCI excluded the null, BCrI embraced the null. BF supported a pooled model in 27 meta-analyses and separate models in 4. The highest density of the posterior model probabilities for 0.333 < Bayes factor < 1 was 0.8. Conclusions In the current meta-analytic cohort, an integrated and multifaceted Bayesian approach gave support to including NRS in a pooled-estimate model. Conversely, caution should attend the reporting of naïve frequentist pooled, RCT and NRS, meta-analytic treatment effects.

This study developed, calibrated, and evaluated a machine learning classifier designed to reduce study identification workload in Cochrane for producing systematic reviews. A machine learning classifier for retrieving randomized controlled trials (RCTs) was developed (the “Cochrane RCT Classifier”), with the algorithm trained using a data set of title–abstract records from Embase, manually labeled by the Cochrane Crowd. The classifier was then calibrated using a further data set of similar records manually labeled by the Clinical Hedges team, aiming for 99% recall. Finally, the recall of the calibrated classifier was evaluated using records of RCTs included in Cochrane Reviews that had abstracts of sufficient length to allow machine classification. The Cochrane RCT Classifier was trained using 280,620 records (20,454 of which reported RCTs). A classification threshold was set using 49,025 calibration records (1,587 of which reported RCTs), and our bootstrap validation found the classifier had recall of 0.99 (95% confidence interval 0.98–0.99) and precision of 0.08 (95% confidence interval 0.06–0.12) in this data set. The final, calibrated RCT classifier correctly retrieved 43,783 (99.5%) of 44,007 RCTs included in Cochrane Reviews but missed 224 (0.5%). Older records were more likely to be missed than those more recently published. The Cochrane RCT Classifier can reduce manual study identification workload for Cochrane Reviews, with a very low and acceptable risk of missing eligible RCTs. This classifier now forms part of the Evidence Pipeline, an integrated workflow deployed within Cochrane to help improve the efficiency of the study identification processes that support systematic review production. •Systematic review processes need to become more efficient.•Machine learning is sufficiently mature for real-world use.•A machine learning classifier was built using data from Cochrane Crowd.•It was calibrated to achieve very high recall.•It is now live and in use in Cochrane review production systems.

The evaluation of the credibility of results from a meta-analysis has become an important part of the evidence synthesis process. We present a methodological framework to evaluate confidence in the results from network meta-analyses, Confidence in Network Meta-Analysis (CINeMA), when multiple interventions are compared. CINeMA considers 6 domains: (i) within-study bias, (ii) reporting bias, (iii) indirectness, (iv) imprecision, (v) heterogeneity, and (vi) incoherence. Key to judgments about within-study bias and indirectness is the percentage contribution matrix, which shows how much information each study contributes to the results from network meta-analysis. The contribution matrix can easily be computed using a freely available web application. In evaluating imprecision, heterogeneity, and incoherence, we consider the impact of these components of variability in forming clinical decisions. Via 3 examples, we show that CINeMA improves transparency and avoids the selective use of evidence when forming judgments, thus limiting subjectivity in the process. CINeMA is easy to apply even in large and complicated networks.

In pragmatic trials, participants are broadly representative of people who will receive a treatment or diagnostic strategy, and the outcomes affect day-to-day care. The authors review the unique features of pragmatic trials through a wide-ranging series of exemplar trials. Pragmatism in clinical trials arose from concerns that many trials did not adequately inform practice because they were optimized to determine efficacy. 1 Because such trials were performed with relatively small samples at sites with experienced investigators and highly selected participants, they could be overestimating benefits and underestimating harm. This led to the belief that more pragmatic trials, designed to show the real-world effectiveness of the intervention in broad patient groups, were required. Medical researchers, both academic and commercial, must deliver health care innovations (drugs, devices, or other interventions) that are safe, beneficial, and cost-effective, and they must identify the subgroups . . .

Consensus on the best treatment regimens for patients with isoniazid-resistant TB is limited; global treatment guidelines differ. We undertook a systematic review and meta-analysis using mixed-treatment comparisons methodology to provide an up-to-date summary of randomised controlled trials (RCTs) and relative regimen efficacy. Ovid MEDLINE, the Web of Science and EMBASE were mined using search terms for TB, drug therapy and RCTs. Extracted data were inputted into fixed-effects and random-effects models. ORs for all possible network comparisons and hierarchical rankings for different regimens were obtained. 12 604 records were retrieved and 118 remained postextraction, representing 59 studies-27 standalone and 32 with multiple papers. In comparison to a baseline category that included the WHO-recommended regimen for countries with high levels of isoniazid resistance (rifampicin-containing regimens using fewer than three effective drugs at 4 months, in which rifampicin was protected by another effective drug at 6 months, and rifampicin was taken for 6 months), extending the duration of rifampicin and increasing the number of effective drugs at 4 months lowered the odds of unfavourable outcomes (treatment failure or the lack of microbiological cure; relapse post-treatment; death due to TB) in a fixed-effects model (OR 0.31 (95% credible interval 0.12-0.81)). In a random-effects model all estimates crossed the null. Our systematic review and network meta-analysis highlight a regimen category that may be more efficacious than the WHO population level recommendation, and identify knowledge gaps where data are sparse. PROSPERO CRD42014015025.

PRECIS is a tool to help trialists make design decisions consistent with the intended purpose of their trial. This paper gives guidance on how to use an improved, validated version, PRECIS-2, which has been developed with the help of over 80 international trialists, clinicians, and policymakers. Keeping the original simple wheel format, PRECIS-2 has nine domains—eligibility criteria, recruitment, setting, organisation, flexibility (delivery), flexibility (adherence), follow-up, primary outcome, and primary analysis—scored from 1 (very explanatory) to 5 (very pragmatic) to facilitate domain discussion and consensus. It is hoped PRECIS-2 will be valuable in supporting the explicit matching of design decisions to how the trial results are intended to be used

Background Subphenotypes were recently reported within clinical acute respiratory distress syndrome (ARDS), with distinct outcomes and therapeutic responses. Experimental models have long been used to mimic features of ARDS pathophysiology, but the presence of distinct subphenotypes among preclinical ARDS remains unknown. This review will investigate whether: 1) subphenotypes can be identified among preclinical ARDS models; 2) such subphenotypes can identify some responsive traits. Methods We will include comparative preclinical (in vivo and ex vivo) ARDS studies published between 2009 and 2019 in which pre-specified therapies were assessed (interleukin (IL)-10, IL-2, stem cells, beta-agonists, corticosteroids, fibroblast growth factors, modulators of the receptor for advanced glycation end-products pathway, anticoagulants, and halogenated agents) and outcomes compared to a control condition. The primary outcome will be a composite of the four key features of preclinical ARDS as per the American Thoracic Society consensus conference (histologic evidence of lung injury, altered alveolar-capillary barrier, lung inflammatory response, and physiological dysfunction). Secondary outcomes will include the single components of the primary composite outcome, net alveolar fluid clearance, and death. MEDLINE, Embase, and Cochrane databases will be searched electronically and data from eligible studies will be extracted, pooled, and analyzed using random-effects models. Individual study reporting will be assessed according to the Animal Research: Reporting of In Vivo Experiments guidelines. Meta-regressions will be performed to identify subphenotypes prior to comparing outcomes across subphenotypes and treatment effects. Discussion This study will inform on the presence and underlying pathophysiological features of subphenotypes among preclinical models of ARDS and should help to determine whether sufficient evidence exists to perform preclinical trials of subphenotype-targeted therapies, prior to potential clinical translation. Systematic review registration PROSPERO (ID: CRD42019157236 ).