While it is important for the evidence supporting practice guidelines to be current, that is often not the case. The advent of living systematic reviews has made the concept of "living guidelines" realistic, with the promise to provide timely, up-to-date and high-quality guidance to target users. We define living guidelines as an optimization of the guideline development process to allow updating individual recommendations as soon as new relevant evidence becomes available. A major implication of that definition is that the unit of update is the individual recommendation and not the whole guideline. We then discuss when living guidelines are appropriate, the workflows required to support them, the collaboration between living systematic reviews and living guideline teams, the thresholds for changing recommendations, and potential approaches to publication and dissemination. The success and sustainability of the concept of living guideline will depend on those of its major pillar, the living systematic review. We conclude that guideline developers should both experiment with and research the process of living guidelines.

Historically, obese individuals were believed to have lower energy expenditure (EE) rates than nonobese individuals (normal and overweight), which, in the long term, would contribute to a positive energy balance and subsequent weight gain. The aim of this review was to critically appraise studies that compared measures of EE and its components, resting EE (REE), activity EE (AEE), and diet-induced thermogenesis (DIT), in obese and nonobese adults to elucidate whether obesity is associated with altered EE. Contrary to popular belief, research has shown that obese individuals have higher absolute REE and total EE. When body composition (namely the metabolically active component, fat-free mass) is taken into account, these differences between obese and nonobese individuals disappear, suggesting that EE in obese individuals is not altered. However, an important question is whether AEE is lower in obese individuals because of a decrease in overall physical activity or because of less energy expended while performing physical activity. AEE and DIT could be reduced in obese individuals, mostly because of unhealthy behavior (low physical activity, higher intake of fat). However, the current evidence does not support the hypothesis that obesity is sustained by lower daily EE or REE. Future studies, comparing EE between obese and nonobese and assessing potential physiologic abnormalities in obese individuals, should be able to better answer the question of whether these individuals have altered energy metabolism.

BackgroundWe explored the performance of three machine learning tools designed to facilitate title and abstract screening in systematic reviews (SRs) when used to (a) eliminate irrelevant records (automated simulation) and (b) complement the work of a single reviewer (semi-automated simulation). We evaluated user experiences for each tool.MethodsWe subjected three SRs to two retrospective screening simulations. In each tool (Abstrackr, DistillerSR, RobotAnalyst), we screened a 200-record training set and downloaded the predicted relevance of the remaining records. We calculated the proportion missed and workload and time savings compared to dual independent screening. To test user experiences, eight research staff tried each tool and completed a survey.ResultsUsing Abstrackr, DistillerSR, and RobotAnalyst, respectively, the median (range) proportion missed was 5 (0 to 28) percent, 97 (96 to 100) percent, and 70 (23 to 100) percent for the automated simulation and 1 (0 to 2) percent, 2 (0 to 7) percent, and 2 (0 to 4) percent for the semi-automated simulation. The median (range) workload savings was 90 (82 to 93) percent, 99 (98 to 99) percent, and 85 (85 to 88) percent for the automated simulation and 40 (32 to 43) percent, 49 (48 to 49) percent, and 35 (34 to 38) percent for the semi-automated simulation. The median (range) time savings was 154 (91 to 183), 185 (95 to 201), and 157 (86 to 172) hours for the automated simulation and 61 (42 to 82), 92 (46 to 100), and 64 (37 to 71) hours for the semi-automated simulation. Abstrackr identified 33–90% of records missed by a single reviewer. RobotAnalyst performed less well and DistillerSR provided no relative advantage. User experiences depended on user friendliness, qualities of the user interface, features and functions, trustworthiness, ease and speed of obtaining predictions, and practicality of the export file(s).ConclusionsThe workload savings afforded in the automated simulation came with increased risk of missing relevant records. Supplementing a single reviewer’s decisions with relevance predictions (semi-automated simulation) sometimes reduced the proportion missed, but performance varied by tool and SR. Designing tools based on reviewers’ self-identified preferences may improve their compatibility with present workflows.Systematic review registrationNot applicable.

Great discrepancies exist in the reported prevalence of altered energy metabolism (hypo- or hypermetabolism) in cancer patients, which is likely due to the vast array of phenomena that can affect energy expenditure in these patients. The purpose of this review was to critically evaluate key determinants of energy expenditure in cancer and the relevance for clinical practice. Resting energy expenditure (REE) is the largest and most commonly measured component of total energy expenditure. In addition to the energetic demand of the tumor itself, REE may be increased due to changes in inflammation, body composition and brown adipose tissue activation. Energy expenditure from physical activity is often lower in cancer compared with healthy populations, and there is evidence to suggest that the thermic effect of food might also be blunted and affected by cancer therapy. Although accurate assessment of energy metabolism is a cornerstone of adequate nutritional therapy, prediction methods often do not capture the true energy expenditure of most cancer patients. In fact, limits of agreement of prediction equations may range from 40% below to 30% above measured REE. Such variability highlights the need for a more comprehensive understanding of energy expenditure in cancer and the value of accurately assessing the energy needs of these patients.

Overnutrition and undernutrition are major concerns in the short and long term for children with cancer. Children treated for cancer have increased fat mass and decreased body cell mass, which are evident during treatment and in survivorship. This trial was registered at http://www.ANZCTR.org.au as ACTRN12614001279617 and ACTRN12614001269628.

Background: We investigated the feasibility of using a machine learning tool's relevance predictions to expedite title and abstract screening. Methods: We subjected 11 systematic reviews and six rapid reviews to four retrospective screening simulations (automated and semi-automated approaches to single-reviewer and dual independent screening) in Abstrackr, a freely-available machine learning software. We calculated the proportion missed, workload savings, and time savings compared to single-reviewer and dual independent screening by human reviewers. We performed cited reference searches to determine if missed studies would be identified via reference list scanning. Results: For systematic reviews, the semi-automated, dual independent screening approach provided the best balance of time savings (median (range) 20 (3-82) hours) and reliability (median (range) proportion missed records, 1 (0-14)%). The cited references search identified 59% (n = 10/17) of the records missed. For the rapid reviews, the fully and semi-automated approaches saved time (median (range) 9 (2-18) hours and 3 (1-10) hours, respectively), but less so than for the systematic reviews. The median (range) proportion missed records for both approaches was 6 (0-22)%. Conclusion:Using Abstrackr to assist one of two reviewers in systematic reviews saves time with little risk of missing relevant records. Many missed records would be identified via other means.

BackgroundIt is evident from previous research that the role of dietary composition in relation to the development of childhood obesity remains inconclusive. Several studies investigating the relationship between body mass index (BMI), waist circumference (WC) and/or skin fold measurements with energy intake have suggested that the macronutrient composition of the diet (protein, carbohydrate, fat) may play an important contributing role to obesity in childhood as it does in adults. This study investigated the possible relationship between BMI and WC with energy intake and percentage energy intake from macronutrients in Australian children and adolescents.MethodsHeight, weight and WC measurements, along with 24 h food and drink records (FDR) intake data were collected from 2460 boys and girls aged 5-17 years living in the state of Queensland, Australia.ResultsStatistically significant, yet weak correlations between BMI z-score and WC with total energy intake were observed in grades 1, 5 and 10, with only 55% of subjects having a physiologically plausible 24 hr FDR. Using Pearson correlations to examine the relationship between BMI and WC with energy intake and percentage macronutrient intake, no significant correlations were observed between BMI z-score or WC and percentage energy intake from protein, carbohydrate or fat. One way ANOVAs showed that although those with a higher BMI z-score or WC consumed significantly more energy than their lean counterparts.ConclusionNo evidence of an association between percentage macronutrient intake and BMI or WC was found. Evidently, more robust longitudinal studies are needed to elucidate the relationship linking obesity and dietary intake.

Measures of body weight and anthropometrics such as body mass index (BMI) are commonly used to assess nutritional status in clinical conditions including cancer. Extensive research has evaluated associations between body weight and prognosis in ovarian cancer patients, yet little is known about the potential impact of body composition (fat mass (FM) and fat-free mass (FFM)) in these patients. Thus, the purpose of this publication was to review the literature (using PubMed and EMBASE) evaluating the impact of body weight and particularly body composition on surgical complications, morbidity, chemotherapy dosing and toxicity (as predictors of prognosis), and survival in ovarian cancer patients. Body weight is rarely associated with intra-operative complications, but obesity predicts higher rates of venous thromboembolism and wound complications post-operatively in ovarian cancer patients. Low levels of FM and FFM are superior predictors of length of hospital stay compared to measures of body weight alone, but the role of body composition on other surgical morbidities is unknown. Obesity complicates chemotherapy dosing due to altered pharmacokinetics, imprecise dosing strategies, and wide variability in FM and FFM. Measurement of body composition has the potential to reduce toxicity if the results are incorporated into chemotherapy dosing calculations. Some findings suggest that excess body weight adversely affects survival, while others find no such association. Limited studies indicate that FM is a better predictor of survival than body weight in ovarian cancer patients, but the direction of this relationship has not been determined. In conclusion, body composition as an indicator of nutritional status is a better prognostic tool than body weight or BMI alone in ovarian cancer patients.