Enhancing a somatic maturity prediction model

Purpose: Assessing biological maturity in studies of children is challenging. Sex-specific regression equations developed using anthropometric measures are widely used to predict somatic maturity. However, prediction accuracy was not established in external samples. Thus, we aimed to evaluate the fit of these equations, assess for overfitting (adjusting as necessary), and calibrate using external samples. Methods: We evaluated potential overfitting using the original Pediatric Bone Mineral Accrual Study (PBMAS; 79 boys and 72 girls; 7.5–17.5 yr). We assessed change in R2 and standard error of the estimate (SEE) with the addition of predictor variables. We determined the effect of within-subject correlation using cluster-robust variance and fivefold random splitting followed by forward-stepwise regression. We used dominant predictors from these splits to assess predictive abilities of various models. We calibrated using participants from the Healthy Bones Study III (HBS-III; 42 boys and 39 girls; 8.9–18.9 yr) and Harpenden Growth Study (HGS; 38 boys and 32 girls; 6.5–19.1 yr). Results: Change in R2 and SEE was negligible when later predictors were added during step-by-step refitting of the original equations, suggesting overfitting. After redevelopment, new models included age × sitting height for boys (R2, 0.91; SEE, 0.51) and age × height for girls (R2, 0.90; SEE, 0.52). These models calibrated well in external samples; HBS boys: b0, 0.04 (0.05); b1, 0.98 (0.03); RMSE, 0.89; HBS girls: b0, 0.35 (0.04); b1, 1.01 (0.02); RMSE, 0.65; HGS boys: b0, −0.20 (0.02); b1, 1.02 (0.01); RMSE, 0.85; HGS girls: b0, −0.02 (0.03); b1, 0.97 (0.02); RMSE, 0.70; where b0 equals calibration intercept (standard error (SE)) and b1 equals calibration slope (SE), and RMSE equals root mean squared error (of prediction). We subsequently developed an age × height alternate for boys, allowing for predictions without sitting height. Conclusion: Our equations provided good fits in external samples and provide an alternative to commonly used models. Original prediction equations were simplified with no meaningful increase in estimation error

Research data management in academic institutions: A scoping review

<div><p>Objective</p><p>The purpose of this study is to describe the volume, topics, and methodological nature of the existing research literature on research data management in academic institutions.</p><p>Materials and methods</p><p>We conducted a scoping review by searching forty literature databases encompassing a broad range of disciplines from inception to April 2016. We included all study types and data extracted on study design, discipline, data collection tools, and phase of the research data lifecycle.</p><p>Results</p><p>We included 301 articles plus 10 companion reports after screening 13,002 titles and abstracts and 654 full-text articles. Most articles (85%) were published from 2010 onwards and conducted within the sciences (86%). More than three-quarters of the articles (78%) reported methods that included interviews, cross-sectional, or case studies. Most articles (68%) included the <i>Giving Access to Data</i> phase of the UK Data Archive Research Data Lifecycle that examines activities such as sharing data. When studies were grouped into five dominant groupings (Stakeholder, Data, Library, Tool/Device, and Publication), data quality emerged as an integral element.</p><p>Conclusion</p><p>Most studies relied on self-reports (interviews, surveys) or accounts from an observer (case studies) and we found few studies that collected empirical evidence on activities amongst data producers, particularly those examining the impact of research data management interventions. As well, fewer studies examined research data management at the early phases of research projects. The quality of all research outputs needs attention, from the application of best practices in research data management studies, to data producers depositing data in repositories for long-term use.</p></div

Additional file 1: of Sustainability of knowledge translation interventions in healthcare decision-making: a scoping review

KT Sustainability Chronic Conditions of Interest. (PDF 77 kb

Groupings of articles.

<p>Groupings of articles.</p

Characteristics of included studies.

<p>Characteristics of included studies.</p

Additional file 1 of Interventions on gender equity in the workplace: a scoping review

Additional file 1: Appendix 1. PRISMA ScR Checklist. Appendix 2. SAGER Guidelines. Appendix 3. SIITHIA Checklist. Appendix 4. GRIPP2 Reporting Checklist. Appendix 5. Database Search Strategies. Appendix 6. Grey Literature Sources. Appendix 7. L1 Screening Form for Titles and Abstracts. Appendix 8. L2 Screening Form for Full-Text Articles. Appendix 9. Data Abstraction Form. Appendix 10. Closely Related but Ultimately Excluded Studies. Appendix 11. Participants Characteristics. Appendix 12. Participants Characteristics. Appendix 13. PROGRESS Plus Table. Appendix 14. Definitions of Gender and Sex. Appendix 15. Intervention Characteristics. Appendix 16. Details of Intervention Outcomes and Results. Appendix 17. Outcomes Examined in Included Studies. Appendix 18. Patient Partner Lay Summary