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In naturally occurring numbers the frequencies of digits 1–9 in the leading position are counterintuitively distributed because the frequencies of occurrence are unequal. Benford-Newcomb’s law describes the expected distribution of these frequencies. It was previously shown that known fraudulent articles consistently violated this law. To compare the features of 12 known fraudulent articles from a single Japanese author to the features of 13 articles in the same research field from other Japanese authors, published during the same time period and identified with a Medline database search. All 25 articles were assessed to determine whether the data violated the law. Formulas provided by the law were used to determine the frequencies of occurrence for the first two leading digits in manually extracted numbers. It was found that all the known fraudulent papers violated the law and 6 of the 13 articles used for comparison followed the law. Assuming that the articles in the comparison group were not falsified or fabricated, the sensitivity of assessing articles with Benford-Newcomb’s law was 100% (95% confidence interval CI: 73.54–100%) but the specificity was only 46.15% (95% CI: 19.22–74.87%) and the positive predictive value was 63.16% (95% CI: 38.36–83.71%). All 12 of the known falsified articles violated Benford-Newcomb’s law, which indicated that this analysis had a high sensitivity. The low specificity of the assessment may be explained by the assumptions made about the articles identified for comparison. Violations of Benford-Newcomb’s law about the frequencies of the leading digits cannot serve as proof of falsification but they may provide a basis for deeper discussions between the editor and author about a submitted work.

The plotting of data into graphs should be a mandatory step in all data analysis as part of a descriptive statistics exercise, since it gives the researcher an overview of the shape and nature of the data. Moreover, outlier values may be identified, which may be incorrect data, or true outliers, from which important findings (and publications) may arise. This exercise should always precede inferential statistics, when possible, and this paper in the Early Human Development WASP series provides some pointers for doing so in Microsoft Excel™.

Plotting data into graphs is a crucial step in data analysis as part of an initial descriptive statistics exercise since it gives the researcher an overview of the shape and nature of the data. Outlier values may also be identified, and these may be incorrect data, or true and important outliers. This paper explains how to access Microsoft Excel's Analysis Toolpak and provides some pointers for the utilisation of the histogram tool within the Toolpak.

The explosion of new methods in science has put additional pressure on authors, readers, and peer reviewers to evaluate the rigor and reproducibility of these new approaches. This issue brings together a collection of articles aimed to stimulate our thinking about how to evaluate the quality of these new methodologies, in the form of several expert reviews on domains ranging from large scale genetics to clinical interventions. These expert reviews are followed by a collection of papers intended to illustrate new or underrepresented methods in our field and include novel approaches to clinical trials to the quantification of text data collected from public repositories online. The issue concludes with a number of original data papers applying network analysis to study comorbidity and symptom status in eating disorder and related populations and a couple of novel neuroimaging papers demonstrating the use of new and promising methods for studying heterogeneity and the use of psychophysiology to answer questions about emotion in eating disorder populations.

We suspect many of our readers will be familiar with the cult TV show The Prisoner, in which actor Patrick McGoohan had his identity taken away by unknown assailants for unknown reasons, and his pleas of “I am not a number, I am a person” (http://www.youtube.com/watch?v=29JewlGsYxs&feature=related) were greeted with variants of “whatever you say, number six.” We would suggest that, as scientists, we are in a situation where the opposite will soon be true, at least for the purposes of scientific scholarship. Scientists will want to be assigned a number, or at least a unique identifier. Why? Imagine a time when you and your complete scholarly output—papers, grant applications, blog posts, etc.—could be identified online and in perpetuity and returned in a variety of easy-to-digest ways. While ego comes into it as a driver to make this happen, measuring scientific career advancement is something that lacks good metrics in a digital world. Unless one has a truly unique name, applying such a metric is not possible now. Even with a unique name, what is the guarantee that all of our scholarly output will be captured by one source of that information? In the end, we as individuals are the only ones who reliably track our scholarly output. This situation is beginning to change, and, as we shall see, new metrics have the promise of much more than simply returning references to our collective life's work as currently described by research papers, research proceedings, books, and book chapters. Although even a complete and current resume generated on demand would be a big step, if it could be returned in a variety of formats for a variety of purposes. These complete resumes are something many of us spend endless hours generating. The idea of having our scholarly output properly characterized is not out of reach, since the articles we write are already identified uniquely by a Digital Object Identifier (DOI; discussed further below). A book or journal is identified by an ISBN, and citations are identified by PubMed identifiers, and so on. The ideas discussed here simply take this identification process for individual publications and citations to the point of providing unique descriptors for each author and to uniquely identify all of each author's scholarly work.

Introduction: The use of group work assessment in medical education is becoming increasingly important to assess the competency of collaborator. However, debate continues on whether this does justice to individual development and assessment. This paper focuses on assessing the individual component within group work. Method: An integrative literature review was conducted and complemented with a survey among representatives of all medical schools in the Netherlands to investigate current practices. Results: The 14 studies included in our review show that an individual component is mainly assessed by peer assessment of individual contributions. Process and product of group work were seldom used separately as criteria. The individual grade is most often based on a group grade and an algorithm to incorporate peer grades. The survey provides an overview of best practices and recommendations for implementing group work assessment. Discussion: The main pitfall when using peer assessment for group work assessment lies in differentiating between the group work process and the resulting product of the group work. Hence, clear criteria are needed to avoid measuring only effort. Decisions about how to weigh assessment of the product and peer assessment of individual contribution should be carefully made and based on predetermined learning goals.

Most doctors finish their careers without ever having published a scientific paper. The small percent that do mostly end up writing “case reports”. But, unless the report describes some truly extraordinary event, very little ever comes of it, and their publication may do more harm than good. The problem is particularly acute in cases of alleged drug toxicity. Case reports are incomplete, uncontrolled, retrospective, lack operational criteria for identifying when an adverse event has actually occurred, and resemble nothing so much as hearsay evidence, a type of evidence that is prohibited in all courts in all of industrialized societies. When a journal, even a highly respected one, decides to publish hearsay, readers are utterly reliant on the integrity of the journal, the author, and the peer review process; such reliance may not always be warranted. Some recent examples of process failure are provided, as well as some suggestions about possible remedies, including the use of pharmaco-vigilance algorithms.

The purpose of this article is to provide the clinician an evidence/experience-based algorithm for the management of stress fractures.Medline search of peer reviewed publications regarding stress fracture etiology, classification, treatment, and natural history.The algorithm was developed from a review of retrospective case series, a few evidence-based papers, and the clinical experience of 4 sports medicine team physicians with a combined experience of over 40 years in the care of athletes at the college and professional level. The literature is almost entirely case series without control groups; therefore, clinical consensus is included as the next best guide to treatment.The emphasis of this article is to provide a clear and simple approach to the management of these fractures by classifying them as either high-risk or low-risk. This separation into 2 groups is based on the biomechanical environment and natural history of the fracture. High-risk stress fractures occur in the superolateral femoral neck, anterior tibial shaft, tarsal navicular, proximal fifth metatarsal, and talar neck. Low-risk stress fractures occur in the lateral malleolus, calcaneus, 2nd through 4th metatarsals, and the femoral shaft.The undertreatment of high-risk stress fractures can lead to catastrophic bone failure and/or prolonged loss of playing time. Overtreatment of low-risk stress fractures can result in unnecessary deconditioning and unneeded loss of playing time. We propose that the use of the simple and clinically relevant algorithm will help guide appropriate management and return to play decision-making as well as encourage future prospective research.

Applying a computerized algorithm to administrative data to help assess the quality of hospital care is intriguing. As Iezzoni and colleagues point out, there are major differences of opinion as to the worth of such efforts. This article significantly advances the state of the art in using administrative data to screen for potential quality-of-care problems. In addition, this work on identifying complications of care goes well beyond the emphasis of many government organizations on hospital mortality rates. One question, however, not raised in the paper is: What is a practical upper limit to the sensitivity and specificity in comparing computerized screen results with the consensus judgments of a group of independent physicians? Advanced statistical techniques (such as bootstrapping) might be used to estimate the stability of consensus judgments by physician groups. When the judgments of two groups of physicians are compared with each other, the resulting sensitivity and specificity will not be .99! In addition, more training of members of the physician panels would probably have increased interrater reliability. While acknowledging this problem, the researchers' detailed analysis of the panel results is intriguing and represents a model for such studies. It is hoped that the authors will follow up on the avenues opened here. Furthermore, what degree of accuracy is necessary to identify facilities with higher-than-expected rates of complications? The authors discuss problems involved in using administrative data to target hospitals and departments for more costly in-depth reviews of quality. It is hoped that the promising findings that are reported here will be validated in other studies. Certainly their algorithms should find a ready audience in insurers and hospitals willing to try them out. Finally, should we expect additional research to lead to improvement in the authors' algorithms? I believe the algorithms will prove difficult to improve upon; but perhaps we should not worry about this. At some point, however, the cost of trying to identify and correct quality problems in "minimally outlier" hospitals will exceed the benefits, particularly given alternative uses for the funds. Might we now be close the the "flat of the curve" in the development of such systems for identification of quality problems? This issue should be discussed much further in future studies.