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The visionary talents of writer Chris Claremont and legendary illustrator Bill Sienkiewicz bring the Demon Bear that has haunted Danielle Moonstar's dreams to horrifying life! It took her parents, and now it has returned for Dani - and only the combined efforts of her fellow New Mutants can stop it from finishing the job! Sink your teeth into a true classic! Then, Dani's nightmare returns years later as San Francisco - and her new team X-Force, come under attack from a similarly unholy ursine!

Lack of evidence-based outcomes data leads to uncertainty in developing treatment regimens in children who are newly diagnosed with ulcerative colitis. We hypothesised that pretreatment clinical, transcriptomic, and microbial factors predict disease course. In this inception cohort study, we recruited paediatric patients aged 4–17 years with newly diagnosed ulcerative colitis from 29 centres in the USA and Canada. Patients initially received standardised mesalazine or corticosteroids, with pre-established criteria for escalation to immunomodulators (ie, thiopurines) or anti-tumor necrosis factor-α (TNFα) therapy. We used RNA sequencing to define rectal gene expression before treatment, and 16S sequencing to characterise rectal and faecal microbiota. The primary outcome was week 52 corticosteroid-free remission with no therapy beyond mesalazine. We assessed factors associated with the primary outcome using logistic regression models of the per-protocol population. This study is registered with ClinicalTrials.gov, number NCT01536535. Between July 10, 2012, and April 21, 2015, of 467 patients recruited, 428 started medical therapy, of whom 400 (93%) were evaluable at 52 weeks and 386 (90%) completed the study period with no protocol violations. 150 (38%) of 400 participants achieved week 52 corticosteroid-free remission, of whom 147 (98%) were taking mesalazine and three (2%) were taking no medication. 74 (19%) of 400 were escalated to immunomodulators alone, 123 (31%) anti-TNFα therapy, and 25 (6%) colectomy. Low baseline clinical severity, high baseline haemoglobin, and week 4 clinical remission were associated with achieving week 52 corticosteroid-free remission (n=386, logistic model area under the curve [AUC] 0·70, 95% CI 0·65–0·75; specificity 77%, 95% CI 71–82). Baseline severity and remission by week 4 were validated in an independent cohort of 274 paediatric patients with newly diagnosed ulcerative colitis. After adjusting for clinical predictors, an antimicrobial peptide gene signature (odds ratio [OR] 0·57, 95% CI 0·39–0·81; p=0·002) and abundance of Ruminococcaceae (OR 1·43, 1·02–2·00; p=0·04), and Sutterella (OR 0·81, 0·65–1·00; p=0·05) were independently associated with week 52 corticosteroid-free remission. Our findings support the utility of initial clinical activity and treatment response by 4 weeks to predict week 52 corticosteroid-free remission with mesalazine alone in children who are newly diagnosed with ulcerative colitis. The development of personalised clinical and biological signatures holds the promise of informing ulcerative colitis therapeutic decisions. US National Institutes of Health.

To review the literature on strategies implemented or identified to prevent or reduce gender bias in peer review of research grants. Studies of any type of qualitative or quantitative design examining interventions to reduce or prevent gender bias during the peer review of health-related research grants were included. Electronic databases including MEDLINE, EMBASE, Education Resources Information Center (ERIC), PsycINFO, Joanna Briggs, the Cochrane Library, Evidence Based Medicine (EBM) Reviews, and the Campbell Library were searched from 2005 to April 2016. A search for grey (i.e., difficult to locate or unpublished) literature was conducted and experts in the field were consulted to identify additional potentially relevant articles. Two individuals screened titles and abstracts, full-text articles, and abstracted data with discrepancies resolved by a third person consistently. After screening 5524 citations and 170 full-text articles, one article evaluating gender-blinding of grant applications using an uncontrolled before-after study design was included. In this study, 891 applications for long-term fellowships in 2006 were included and 47% of the applicants were women. These were scored by 13 peer reviewers (38% were women). The intervention included eliminating references to gender from the applications, letters of recommendations, and interview reports that were sent to the committee members for evaluation. The proportion of successful applications led by women did not change with gender-blinding, although the number of successful applications that were led by men increased slightly. There is limited research on interventions to mitigate gender bias in the peer review of grants. Only one study was identified and no difference in the proportion of women who were successful in receiving grant funding was observed. Our results suggest that interventions to prevent gender bias should be adapted and tested in the context of grant peer review to determine if they will have an impact.

ObjectivesTo evaluate the comparative effectiveness and safety of intravitreal bevacizumab, ranibizumab and aflibercept for patients with choroidal neovascular age-related macular degeneration (cn-AMD), diabetic macular oedema (DMO), macular oedema due to retinal vein occlusion (RVO-MO) and myopic choroidal neovascularisation (m-CNV).DesignSystematic review and random-effects meta-analysis.MethodsMultiple databases were searched from inception to 17 August 2017. Eligible head-to-head randomised controlled trials (RCTs) comparing the (anti-VEGF) drugs in adult patients aged ≥18 years with the retinal conditions of interest. Two reviewers independently screened studies, extracted data and assessed risk of bias.Results19 RCTs involving 7459 patients with cn-AMD (n=12), DMO (n=3), RVO-MO (n=2) and m-CNV (n=2) were included. Vision gain was not significantly different in patients with cn-AMD, DMO, RVO-MO and m-CNV treated with bevacizumab versus ranibizumab. Similarly, vision gain was not significantly different between cn-AMD patients treated with aflibercept versus ranibizumab. Patients with DMO treated with aflibercept experienced significantly higher vision gain at 12 months than patients receiving ranibizumab or bevacizumab; however, this difference was not significant at 24 months. Rates of systemic serious harms were similar across anti-VEGF agents. Posthoc analyses revealed that an as-needed treatment regimen (6–9 injections per year) was associated with a mortality increase of 1.8% (risk ratio: 2.0 [1.2 to 3.5], 2 RCTs, 1795 patients) compared with monthly treatment in cn-AMD patients.ConclusionsIntravitreal bevacizumab was a reasonable alternative to ranibizumab and aflibercept in patients with cn-AMD, DMO, RVO-MO and m-CNV. The only exception was for patients with DME and low visual acuity (<69 early treatment diabetic retinopathy study [ETDRS] letters), where treatment with aflibercept was associated with significantly higher vision gain (≥15 ETDRS letters) than bevacizumab or ranibizumab at 12 months; but the significant effects were not maintained at 24 months. The choice of anti-VEGF drugs may depend on the specific retinal condition, baseline visual acuity and treatment regimen.PROSPERO registration numberCRD42015022041.

Hereditary hemorrhagic telangiectasia (HHT) is characterized by extensive telangiectasias and arteriovenous malformations. The primary clinical manifestation is epistaxis that results in iron-deficiency anemia and reduced health-related quality of life. We conducted a randomized, placebo-controlled trial to evaluate the safety and efficacy of pomalidomide for the treatment of HHT. We randomly assigned patients, in a 2:1 ratio, to receive pomalidomide at a dose of 4 mg daily or matching placebo for 24 weeks. The primary outcome was the change from baseline through week 24 in the Epistaxis Severity Score (a validated bleeding score in HHT; range, 0 to 10, with higher scores indicating worse bleeding). A reduction of 0.71 points or more is considered clinically significant. A key secondary outcome was the HHT-specific quality-of-life score (range, 0 to 16, with higher scores indicating more limitations). The trial was closed to enrollment in June 2023 after a planned interim analysis met a prespecified threshold for efficacy. A total of 144 patients underwent randomization; 95 patients were assigned to receive pomalidomide and 49 to receive placebo. The baseline mean (±SD) Epistaxis Severity Score was 5.0±1.5, a finding consistent with moderate-to-severe epistaxis. At 24 weeks, the mean difference between the pomalidomide group and the placebo group in the change from baseline in the Epistaxis Severity Score was -0.94 points (95% confidence interval [CI], -1.57 to -0.31; P = 0.004). The mean difference in the changes in the HHT-specific quality-of-life score between the groups was -1.4 points (95% CI, -2.6 to -0.3). Adverse events that were more common in the pomalidomide group than in the placebo group included neutropenia, constipation, and rash. Among patients with HHT, pomalidomide treatment resulted in a significant, clinically relevant reduction in epistaxis severity. No unexpected safety signals were identified. (Funded by the National Heart, Lung, and Blood Institute; PATH-HHT Clinicaltrials.gov number, NCT03910244).

Background We completed a scoping review on the barriers and facilitators to use of systematic reviews by health care managers and policy makers, including consideration of format and content, to develop recommendations for systematic review authors and to inform research efforts to develop and test formats for systematic reviews that may optimise their uptake. Methods We used the Arksey and O’Malley approach for our scoping review. Electronic databases (e.g., MEDLINE, EMBASE, PsycInfo) were searched from inception until September 2014. Any study that identified barriers or facilitators (including format and content features) to uptake of systematic reviews by health care managers and policy makers/analysts was eligible for inclusion. Two reviewers independently screened the literature results and abstracted data from the relevant studies. The identified barriers and facilitators were charted using a barriers and facilitators taxonomy for implementing clinical practice guidelines by clinicians. Results We identified useful information for authors of systematic reviews to inform their preparation of reviews including providing one-page summaries with key messages, tailored to the relevant audience. Moreover, partnerships between researchers and policy makers/managers to facilitate the conduct and use of systematic reviews should be considered to enhance relevance of reviews and thereby influence uptake. Conclusions Systematic review authors can consider our results when publishing their systematic reviews. These strategies should be rigorously evaluated to determine impact on use of reviews in decision-making.

ObjectivesTo identify ML tools in hospital settings and how they were implemented to inform decision-making for patient care through a scoping review. We investigated the following research questions: What ML interventions have been used to inform decision-making for patient care in hospital settings? What strategies have been used to implement these ML interventions?DesignA scoping review was undertaken. MEDLINE, Embase, Cochrane Central Register of Controlled Trials (CENTRAL) and the Cochrane Database of Systematic Reviews (CDSR) were searched from 2009 until June 2021. Two reviewers screened titles and abstracts, full-text articles, and charted data independently. Conflicts were resolved by another reviewer. Data were summarised descriptively using simple content analysis.SettingHospital setting.ParticipantAny type of clinician caring for any type of patient.InterventionMachine learning tools used by clinicians to inform decision-making for patient care, such as AI-based computerised decision support systems or “‘model-based’” decision support systems.Primary and secondary outcome measuresPatient and study characteristics, as well as intervention characteristics including the type of machine learning tool, implementation strategies, target population. Equity issues were examined with PROGRESS-PLUS criteria.ResultsAfter screening 17 386 citations and 3474 full-text articles, 20 unique studies and 1 companion report were included. The included articles totalled 82 656 patients and 915 clinicians. Seven studies reported gender and four studies reported PROGRESS-PLUS criteria (race, health insurance, rural/urban). Common implementation strategies for the tools were clinician reminders that integrated ML predictions (44.4%), facilitated relay of clinical information (17.8%) and staff education (15.6%). Common barriers to successful implementation of ML tools were time (11.1%) and reliability (11.1%), and common facilitators were time/efficiency (13.6%) and perceived usefulness (13.6%).ConclusionsWe found limited evidence related to the implementation of ML tools to assist clinicians with patient healthcare decisions in hospital settings. Future research should examine other approaches to integrating ML into hospital clinician decisions related to patient care, and report on PROGRESS-PLUS items.FundingCanadian Institutes of Health Research (CIHR) Foundation grant awarded to SES and the CIHR Strategy for Patient Oriented-Research Initiative (GSR-154442).Scoping review registrationhttps://osf.io/e2mna.

The aim of the article was to identify and summarize studies assessing methodologies for study selection, data abstraction, or quality appraisal in systematic reviews. A systematic review was conducted, searching MEDLINE, EMBASE, and the Cochrane Library from inception to September 1, 2016. Quality appraisal of included studies was undertaken using a modified Quality Assessment of Diagnostic Accuracy Studies 2, and key results on accuracy, reliability, efficiency of a methodology, or impact on results and conclusions were extracted. After screening 5,600 titles and abstracts and 245 full-text articles, 37 studies were included. For screening, studies supported the involvement of two independent experienced reviewers and the use of Google Translate when screening non-English articles. For data abstraction, studies supported involvement of experienced reviewers (especially for continuous outcomes) and two independent reviewers, use of dual monitors, graphical data extraction software, and contacting authors. For quality appraisal, studies supported intensive training, piloting quality assessment tools, providing decision rules for poorly reported studies, contacting authors, and using structured tools if different study designs are included. Few studies exist documenting common systematic review practices. Included studies support several systematic review practices. These results provide an updated evidence-base for current knowledge synthesis guidelines and methods requiring further research.

Background Current text mining tools supporting abstract screening in systematic reviews are not widely used, in part because they lack sensitivity and precision. We set out to develop an accessible, semi-automated “workflow” to conduct abstract screening for systematic reviews and other knowledge synthesis methods. Methods We adopt widely recommended text-mining and machine-learning methods to (1) process title-abstracts into numerical training data; and (2) train a classification model to predict eligible abstracts. The predicted abstracts are screened by human reviewers for (“true”) eligibility, and the newly eligible abstracts are used to identify similar abstracts, using near-neighbor methods, which are also screened. These abstracts, as well as their eligibility results, are used to update the classification model, and the above steps are iterated until no new eligible abstracts are identified. The workflow was implemented in R and evaluated using a systematic review of insulin formulations for type-1 diabetes (14,314 abstracts) and a scoping review of knowledge-synthesis methods (17,200 abstracts). Workflow performance was evaluated against the recommended practice of screening abstracts by 2 reviewers, independently. Standard measures were examined: sensitivity (inclusion of all truly eligible abstracts), specificity (exclusion of all truly ineligible abstracts), precision (inclusion of all truly eligible abstracts among all abstracts screened as eligible), F1-score (harmonic average of sensitivity and precision), and accuracy (correctly predicted eligible or ineligible abstracts). Workload reduction was measured as the hours the workflow saved, given only a subset of abstracts needed human screening. Results With respect to the systematic and scoping reviews respectively, the workflow attained 88%/89% sensitivity, 99%/99% specificity, 71%/72% precision, an F1-score of 79%/79%, 98%/97% accuracy, 63%/55% workload reduction, with 12%/11% fewer abstracts for full-text retrieval and screening, and 0%/1.5% missed studies in the completed reviews. Conclusion The workflow was a sensitive, precise, and efficient alternative to the recommended practice of screening abstracts with 2 reviewers. All eligible studies were identified in the first case, while 6 studies (1.5%) were missed in the second that would likely not impact the review’s conclusions. We have described the workflow in language accessible to reviewers with limited exposure to natural language processing and machine learning, and have made the code available to reviewers.

In this paper, a prediction model is developed using support vector machine for forecasting the parameter associated with ground motion of a seismic signal. The prediction model is developed using three learning algorithms, ϵ-support vector regression, ν-support vector regression and least square-support vector regression (Ls-SVR) for forecasting peak ground acceleration (PGA), a parameter associated with ground motion of a seismic signal. The prediction model is developed for each of the algorithms with different kernel functions, namely linear kernel, polynomial kernel and radial basis function kernel. The ground motion parameter is related to four seismic parameters, namely faulting mechanism, average soil shear wave velocity, earthquake magnitude and source to site distance. The database used for modelling is NGA flatfile released by Pacific Earthquake Engineering Research Center. The experimental results show that the optimal prediction model for forecasting PGA is Ls-SVR with RBF kernel. It is observed that the developed prediction model is better compared to the existing conventional models and benchmark models in the same database. This paper further compares the three variations of SVR algorithm for ground motion parameter prediction model. The learning effectiveness of each algorithm is measured in terms of accuracy, testing error and overfitness measure.