The age threshold of the 8th edition AJCC classification is useful for indicating patients with aggressive papillary thyroid cancer in clinical practice.

BACKGROUND: Papillary thyroid cancer (PTC) is unique among cancers in that patient age is a consideration in staging. One of the most important modifications in the 8th Edition of the American Joint Committee on Cancer (AJCC) classification is to increase the age cut off for risk stratification in PTC from 45 to 55 years. However, whether this cut off is useful in clinical practice remains controversial. In the present study, we assessed how well this new age threshold stratifies patients with aggressive PTC. METHODS: We retrospectively analyzed the clinicopathological features and overall survival rate of patients with PTC admitted to and surgically treated at a single surgical center. The study protocol was divided into two series. In each series all patients (n = 523) were divided in 2 groups according to age cut off. In the first series (cut off 45) patients < 45 (n = 193) vs. ≥45 (n = 330) were compared, and in the second series (cut off 55) patients < 55 (n = 306) vs. ≥55 (n = 217) were compared. RESULTS: The rate of the prevalence of locally advanced disease (pT3 and pT4) was significantly higher in the patients above 55 years old than in those below 55 years old (p = 0.013). No significant differences were found for this parameter in series with cut off point 45 years old. A significantly higher risk of locally advanced disease T3 + T4 (OR = 4.87) and presence of LNM (N1) (OR = 3.78) was observed in ≥45 years old group (p = 0.021 and p < 0.0001, respectively). More expressive results were found for the patients ≥55 years old group, where the risk of locally advanced disease (T3 + T4) was higher (OR = 5.21) and LNM presence was OR = 4.76 (p < 0.001 and p < 0.0001, respectively). None of the patients below 55 years old showed distant metastasis, but 19 patients above 55 years old showed M1 (p < 0.0001). In older patients group (≥55 years old) we observed deaths related thyroid cancer in 11 individuals. CONCLUSIONS: The age cut off of 55 years old for risk stratification proposed by the 8th Edition of AJCC effectively stratifies PTC patients with a poor prognosis, indicating it is likely to be useful in clinical practice.

Scientific medical journals in Mexico.

This symposium describes the main characteristics of six Mexican scientific journals indexed in Journal Citation Reports: Archives of Medical Research, Revista de Investigación Clínica-Clinical and Translational Investigation, Gaceta Médica de México, Salud Pública de México, Cirugía y Cirujanos and Salud Mental. Particular emphasis is given to their historical and organizational aspects, as well as to their main achievements recognized by the national and international scientific community.En este simposio se describen las principales características de seis revistas científicas mexicanas reconocidas por el. Journal Citation Reports: Archives of Medical Research, Revista de Investigación Clínica-Clinical and Translational Investigation, Gaceta Médica de México, Salud Pública de México, Cirugía y Cirujanos y Salud Mental. Se hace énfasis en sus aspectos históricos y organizacionales, así como en sus logros principales ante la comunidad científica nacional e internacional.

Helicopter Emergency Medical Services Literature 1972-2017: Characteristics and Trends.

Since its inception in the latter part of the 20th century, the rapid expansion of helicopter emergency medical services (HEMS) has been accompanied by remarkable growth in the relevant evidence base. There are many review articles describing lessons contained within the various arenas of HEMS literature, but there is little or no characterization of the numbers and types of publications comprising the HEMS-related evidence base. This study analyzed all indexed publications mentioning HEMS (with abstract included) in the United States National Library of Medicine's PubMed collection. The aims of the analysis were to provide quantitative, qualitative, and longitudinal trend information regarding the 1972 to 2017 evidence base relevant to HEMS.

Indexing by Bibliographic Databases of Journals Published in the Developing World.

The removal of Beall's blog may result in increased numbers of predatory journals and their subsequent victims. Recognizing this, the World Association of Medical Editors (WAME) suggested criteria for identifying predatory journals in a statement issued on February 18, 2017. These criteria may be helpful in the current scenario of scientific publishing. However, a few lapses and limitations need to be taken into account when translating these policies to the situation in developing countries. This letter presents several cases of legitimate journals and platforms from the developing world that may be erroneously categorized as predatory according to the WAME criteria. We also suggest some improvements in these journals' policies.

Detecting Hijacked Journals by Using Classification Algorithms.

Invalid journals are recent challenges in the academic world and many researchers are unacquainted with the phenomenon. The number of victims appears to be accelerating. Researchers might be suspicious of predatory journals because they have unfamiliar names, but hijacked journals are imitations of well-known, reputable journals whose websites have been hijacked. Hijacked journals issue calls for papers via generally laudatory emails that delude researchers into paying exorbitant page charges for publication in a nonexistent journal. This paper presents a method for detecting hijacked journals by using a classification algorithm. The number of published articles exposing hijacked journals is limited and most of them use simple techniques that are limited to specific journals. Hence we needed to amass Internet addresses and pertinent data for analyzing this type of attack. We inspected the websites of 104 scientific journals by using a classification algorithm that used criteria common to reputable journals. We then prepared a decision tree that we used to test five journals we knew were authentic and five we knew were hijacked.

Metrics for Original Research Articles in the AJR: From First Submission to Final Publication.

OBJECTIVE: The objective of our study was to evaluate manuscript metrics pertaining to AJR submissions, assessing the pathway from manuscript submission to publication, including the reviewer allocation time, decisions rendered, timing of decisions rendered, and time to publication. MATERIALS AND METHODS: Six hundred ninety-six unsolicited Original Research manuscripts submitted to the AJR between July 1, 2012, and December 21, 2012, were included in this retrospective analysis. Metrics pertaining to manuscripts' decision status and associated timelines were extracted by journal editorial staff and assessed using standard summary statistics. RESULTS: For new submissions, decisions rendered were as follows: Accept, 0.3%; Minor Revision, 8.5%; Major Revision, 19.7%; Reject, 65.1%; and Reject Without Review, 6.5%. For first and second resubmissions, 40.0-55.2% of manuscripts representing a Major Revision and 91.5-94.7% of manuscripts representing a Minor Revision were accepted; 100% of manuscripts undergoing a third resubmission were accepted; 98.3% and 84.7% of manuscripts receiving at first submission a decision of Minor Revision and Major Revision, respectively, ultimately achieved acceptance. The time (mean ± SD) to review a new submission was 30.5 ± 43.1 days (Accept), 42.7 ± 27.4 days (Minor Revision), 39.4 ± 17.6 days (Major Revision), and 40.2 ± 20.3 days (Reject) and decreased with each subsequent resubmission to 6.3 ± 6.3 days (Accept) for third resubmissions. The mean days for authors to submit a first resubmission was 21.1 ± 15.3 days (Minor Revision) and 73.7 ± 65.1 days (Major Revision) and decreased with each subsequent resubmission to 9.8 ± 11.3 days (Minor Revision) and 27.0 ± 0.0 days (Major Revision) for third resubmissions. The mean time from acceptance to publication was 242.5 ± 47.5 days. CONCLUSION: The observed metrics may provide valuable insights for authors and for AJR editorial staff in ongoing efforts to shorten turnaround times from manuscript submission to publication.

Journal Rankings by Health Management Faculty Members: Are There Differences by Rank, Leadership Status, or Area of Expertise?

Health administration (HA) faculty members publish in a variety of journals, including journals focused on management, economics, policy, and information technology. HA faculty members are evaluated on the basis of the quality and quantity of their journal publications. However, it is unclear how perceptions of these journals vary by subdiscipline, department leadership role, or faculty rank. It is also not clear how perceptions of journals may have changed over the past decade since the last evaluation of journal rankings in the field was published. The purpose of the current study is to examine how respondents rank journals in the field of HA, as well as the variation in perception by academic rank, department leadership status, and area of expertise. Data were drawn from a survey of HA faculty members at U.S. universities, which was completed in 2012. Different journal ranking patterns were noted for faculty members of different subdisciplines. The health management-oriented journals (Health Care Management Review and Journal of Healthcare Management) were ranked higher than in previous research, suggesting that journal ranking perceptions may have changed over the intervening decade. Few differences in perceptions were noted by academic rank, but we found that department chairs were more likely than others to select Health Affairs in their top three most prestigious journals (ß = 0.768; p < .01). Perceived journal prestige varied between a department chair and untenured faculty in different disciplines, and this perceived difference could have implications for promotion and tenure decisions.

Automatic recognition of conceptualization zones in scientific articles and two life science applications.

MOTIVATION: Scholarly biomedical publications report on the findings of a research investigation. Scientists use a well-established discourse structure to relate their work to the state of the art, express their own motivation and hypotheses and report on their methods, results and conclusions. In previous work, we have proposed ways to explicitly annotate the structure of scientific investigations in scholarly publications. Here we present the means to facilitate automatic access to the scientific discourse of articles by automating the recognition of 11 categories at the sentence level, which we call Core Scientific Concepts (CoreSCs). These include: Hypothesis, Motivation, Goal, Object, Background, Method, Experiment, Model, Observation, Result and Conclusion. CoreSCs provide the structure and context to all statements and relations within an article and their automatic recognition can greatly facilitate biomedical information extraction by characterizing the different types of facts, hypotheses and evidence available in a scientific publication. RESULTS: We have trained and compared machine learning classifiers (support vector machines and conditional random fields) on a corpus of 265 full articles in biochemistry and chemistry to automatically recognize CoreSCs. We have evaluated our automatic classifications against a manually annotated gold standard, and have achieved promising accuracies with 'Experiment', 'Background' and 'Model' being the categories with the highest F1-scores (76%, 62% and 53%, respectively). We have analysed the task of CoreSC annotation both from a sentence classification as well as sequence labelling perspective and we present a detailed feature evaluation. The most discriminative features are local sentence features such as unigrams, bigrams and grammatical dependencies while features encoding the document structure, such as section headings, also play an important role for some of the categories. We discuss the usefulness of automatically generated CoreSCs in two biomedical applications as well as work in progress. AVAILABILITY: A web-based tool for the automatic annotation of articles with CoreSCs and corresponding documentation is available online at http://www.sapientaproject.com/software http://www.sapientaproject.com also contains detailed information pertaining to CoreSC annotation and links to annotation guidelines as well as a corpus of manually annotated articles, which served as our training data. CONTACT: liakata@ebi.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Reconceptualizing the classification of PNAS articles.

PNAS article classification is rooted in long-standing disciplinary divisions that do not necessarily reflect the structure of modern scientific research. We reevaluate that structure using latent pattern models from statistical machine learning, also known as mixed-membership models, that identify semantic structure in co-occurrence of words in the abstracts and references. Our findings suggest that the latent dimensionality of patterns underlying PNAS research articles in the Biological Sciences is only slightly larger than the number of categories currently in use, but it differs substantially in the content of the categories. Further, the number of articles that are listed under multiple categories is only a small fraction of what it should be. These findings together with the sensitivity analyses suggest ways to reconceptualize the organization of papers published in PNAS.

Methodological quality and descriptive characteristics of prosthodontic-related systematic reviews.

Ideally, healthcare systematic reviews (SRs) should be beneficial to practicing professionals in making evidence-based clinical decisions. However, the conclusions drawn from SRs are directly related to the quality of the SR and of the included studies. The aim was to investigate the methodological quality and key descriptive characteristics of SRs published in prosthodontics. Methodological quality was analysed using the Assessment of Multiple Reviews (AMSTAR) tool. Several electronic resources (MEDLINE, EMBASE, Web of Science and American Dental Association's Evidence-based Dentistry website) were searched. In total 106 SRs were located. Key descriptive characteristics and methodological quality features were gathered and assessed, and descriptive and inferential statistical testing performed. Most SRs in this sample originated from the European continent followed by North America. Two to five authors conducted most SRs; the majority was affiliated with academic institutions and had prior experience publishing SRs. The majority of SRs were published in specialty dentistry journals, with implant or implant-related topics, the primary topics of interest for most. According to AMSTAR, most quality aspects were adequately fulfilled by less than half of the reviews. Publication bias and grey literature searches were the most poorly adhered components. Overall, the methodological quality of the prosthodontic-related systematic was deemed limited. Future recommendations would include authors to have prior training in conducting SRs and for journals to include a universal checklist that should be adhered to address all key characteristics of an unbiased SR process.

Hypotheses generation as supervised link discovery with automated class labeling on large-scale biomedical concept networks.

Computational approaches to generate hypotheses from biomedical literature have been studied intensively in recent years. Nevertheless, it still remains a challenge to automatically discover novel, cross-silo biomedical hypotheses from large-scale literature repositories. In order to address this challenge, we first model a biomedical literature repository as a comprehensive network of biomedical concepts and formulate hypotheses generation as a process of link discovery on the concept network. We extract the relevant information from the biomedical literature corpus and generate a concept network and concept-author map on a cluster using Map-Reduce frame-work. We extract a set of heterogeneous features such as random walk based features, neighborhood features and common author features. The potential number of links to consider for the possibility of link discovery is large in our concept network and to address the scalability problem, the features from a concept network are extracted using a cluster with Map-Reduce framework. We further model link discovery as a classification problem carried out on a training data set automatically extracted from two network snapshots taken in two consecutive time duration. A set of heterogeneous features, which cover both topological and semantic features derived from the concept network, have been studied with respect to their impacts on the accuracy of the proposed supervised link discovery process. A case study of hypotheses generation based on the proposed method has been presented in the paper.

[Rating and ranking of medical journals: a randomised controlled evaluation of impact factor and number of listed journals]. / Rating und Ranking medizinischer Zeitschriften: Randomisiert kontrollierte Evaluation von Impact Factor und Zahl der gelisteten Journale.

BACKGROUND: The impact factor is a purely bibliometric parameter built on a number of publications and their citations that occur within clearly defined periods. Appropriate interpretation of the impact factor is important as it is also used worldwide for the evaluation of research performance. RESEARCH QUESTION: It is assumed that the number of medical journals reflects the extent of diseases and patient populations involved and that the number is correlated with the level of the impact factor. METHOD: 174 category lists (Subject Categories) are included in the area Health Sciences of the ISI Web of Knowledge of Thomson Reuters, 71 of which belong to the field of medicine and 50 of which have a clinical and/or application-oriented focus. These alphabetically arranged 50 category lists were consecutively numbered, randomized by odd and even numbers, respectively, into 2 equal-sized groups and then grouped according to organ specialities, sub-specialities and cross-disciplinary fields. By tossing up a coin it was decided which group should be evaluated first. Only then the category lists were downloaded and the number of journals, as well as the impact factors of journals ranking number 1 and 2, as well as the impact factors of journals at the end of the first third and at the end of the first half of each category list were compared. RESULTS: The number of journals per category list varies considerably between 5 and 252. The lists of organ specialties and cross-disciplinary fields include more than three times as many journals as those of the sub-specialities; the highest numbers of journals are listed for the cross-disciplinary fields. The level of impact factor of journals that rank number 1 in the lists varies considerably and ranges from 3,058 to 94,333; a similar variability exists for the journals at rank 2. On the other hand, the impact factor of journals at the end of the first third of the lists varies from 1,214 and 3,953, and for those journals at the end of the first half of a respective category list it varies from 0,609 and 2,872. The slope of the straight correlation line between the level of impact factors of journals at rank 1 and 2 with the number of listed journals varies from 0,0756 and 0,2651 (correlation coefficients between 0,49 and 0,96). For the journals ranking further down in the lists the straight correlation lines run almost horizontally or with inverse slope. CONCLUSIONS: This current analysis adds to the knowledge for an appropriate interpretation of the impact factor. Generally, greater importance should be given to the ranking of a journal within a corresponding category list.

Accurate determination of imaging modality using an ensemble of text- and image-based classifiers.

Imaging modality can aid retrieval of medical images for clinical practice, research, and education. We evaluated whether an ensemble classifier could outperform its constituent individual classifiers in determining the modality of figures from radiology journals. Seventeen automated classifiers analyzed 77,495 images from two radiology journals. Each classifier assigned one of eight imaging modalities--computed tomography, graphic, magnetic resonance imaging, nuclear medicine, positron emission tomography, photograph, ultrasound, or radiograph-to each image based on visual and/or textual information. Three physicians determined the modality of 5,000 randomly selected images as a reference standard. A "Simple Vote" ensemble classifier assigned each image to the modality that received the greatest number of individual classifiers' votes. A "Weighted Vote" classifier weighted each individual classifier's vote based on performance over a training set. For each image, this classifier's output was the imaging modality that received the greatest weighted vote score. We measured precision, recall, and F score (the harmonic mean of precision and recall) for each classifier. Individual classifiers' F scores ranged from 0.184 to 0.892. The simple vote and weighted vote classifiers correctly assigned 4,565 images (F score, 0.913; 95% confidence interval, 0.905-0.921) and 4,672 images (F score, 0.934; 95% confidence interval, 0.927-0.941), respectively. The weighted vote classifier performed significantly better than all individual classifiers. An ensemble classifier correctly determined the imaging modality of 93% of figures in our sample. The imaging modality of figures published in radiology journals can be determined with high accuracy, which will improve systems for image retrieval.

What's in a title? An assessment of whether randomized controlled trial in a title means that it is one.

INTRODUCTION: In this study, we aimed to investigate whether studies published in orthodontic journals and titled as randomized clinical trials are truly randomized clinical trials. A second objective was to explore the association of journal type and other publication characteristics on correct classification. METHODS: American Journal of Orthodontics and Dentofacial Orthopedics, European Journal of Orthodontics, Angle Orthodontist, Journal of Orthodontics, Orthodontics and Craniofacial Research, World Journal of Orthodontics, Australian Orthodontic Journal, and Journal of Orofacial Orthopedics were hand searched for clinical trials labeled in the title as randomized from 1979 to July 2011. The data were analyzed by using descriptive statistics, and univariable and multivariable examinations of statistical associations via ordinal logistic regression modeling (proportional odds model). RESULTS: One hundred twelve trials were identified. Of the included trials, 33 (29.5%) were randomized clinical trials, 52 (46.4%) had an unclear status, and 27 (24.1%) were not randomized clinical trials. In the multivariable analysis among the included journal types, year of publication, number of authors, multicenter trial, and involvement of statistician were significant predictors of correctly classifying a study as a randomized clinical trial vs unclear and not a randomized clinical trial. CONCLUSIONS: From 112 clinical trials in the orthodontic literature labeled as randomized clinical trials, only 29.5% were identified as randomized clinical trials based on clear descriptions of appropriate random number generation and allocation concealment. The type of journal, involvement of a statistician, multicenter trials, greater numbers of authors, and publication year were associated with correct clinical trial classification. This study indicates the need of clear and accurate reporting of clinical trials and the need for educating investigators on randomized clinical trial methodology.

Classification and prioritization of biomedical literature for the comparative toxicogenomics database.

We present a new approach to perform biomedical documents classification and prioritization for the Comparative Toxicogenomics Database (CTD). This approach is motivated by needs such as literature curation, in particular applied to the human health environment domain. The unique integration of chemical, genes/proteins and disease data in the biomedical literature may advance the identification of exposure and disease biomarkers, mechanisms of chemical actions, and the complex aetiologies of chronic diseases. Our approach aims to assist biomedical researchers when searching for relevant articles for CTD. The task is functionally defined as a binary classification task, where selected articles must also be ranked by order of relevance. We design a SVM classifier, which combines three main feature sets: an information retrieval system (EAGLi), a biomedical named-entity recognizer (MeSH term extraction), a gene normalization (GN) service (NormaGene) and an ad-hoc keyword recognizer for diseases and chemicals. The evaluation of the gene identification module was done on BioCreativeIII test data. Disease normalization is achieved with 95% precision and 93% of recall. The evaluation of the classification was done on the corpus provided by BioCreative organizers in 2012. The approach showed promising performance on the test data.

Variations in food and drink advertising in UK monthly women's magazines according to season, magazine type and socio-economic profile of readers: a descriptive study of publications over 12 months.

BACKGROUND: Overweight and obesity are recognised nationally and internationally as key public health challenges. Food and drink advertising is one of the array of factors that influence both diet and physical activity choices and, hence, body weight and obesity. Little previous work has focused on food and drink advertising in magazines. We studied food and drink advertising in a wide range of popular UK monthly women's magazines published over a full year. We explored differences in the prevalence of food and drink advertising and the type of food and drinks advertised according to season, magazine type and socio-economic profile of readers. METHODS: All advertisements in all issues of 18 popular UK monthly women's magazines published over 12 months were identified. For each food or drink advertisement, branded food and drinks were noted and categorised into one of seven food groups. All analyses were at the level of the individual advertisement. RESULTS: A total of 35 053 advertisements were identified; 1380 (3.9%) of these were for food or drink. The most common food group represented was 'food and drinks high in fat and/or sugar' (28.0% of food advertisements), the least common group was 'fruits & vegetables' (2.0% of food advertisements). Advertisements for alcohol accounted for 10.1% of all food advertisements. Food and drink advertisements were most common in summer, general interest magazines, and those with the most affluent readerships. There were some differences in the type of food and drink advertised across season, magazine type and socio-economic profile of readers. CONCLUSIONS: Food and drink advertisements represented only a small proportion of advertisements in UK women's monthly magazines. Food and drink advertisements in these magazines feature a high proportion of 'less healthy' foods. There were a number of differences across season, magazine type and according to the socio-economic profile of readers in the prevalence of food and drink advertisements. Fewer differences were seen in the type of food and drinks advertised.