The Use of Social Media to Increase the Impact of Health Research: Systematic Review.

BACKGROUND: Academics in all disciplines increasingly use social media to share their publications on the internet, reaching out to different audiences. In the last few years, specific indicators of social media impact have been developed (eg, Altmetrics), to complement traditional bibliometric indicators (eg, citation count and h-index). In health research, it is unclear whether social media impact also translates into research impact. OBJECTIVE: The primary aim of this study was to systematically review the literature on the impact of using social media on the dissemination of health research. The secondary aim was to assess the correlation between Altmetrics and traditional citation-based metrics. METHODS: We conducted a systematic review to identify studies that evaluated the use of social media to disseminate research published in health-related journals. We specifically looked at studies that described experimental or correlational studies linking the use of social media with outcomes related to bibliometrics. We searched the Medical Literature Analysis and Retrieval System Online (MEDLINE), Excerpta Medica dataBASE (EMBASE), and Cumulative Index to Nursing and Allied Health Literature (CINAHL) databases using a predefined search strategy (International Prospective Register of Systematic Reviews: CRD42017057709). We conducted independent and duplicate study selection and data extraction. Given the heterogeneity of the included studies, we summarized the findings through a narrative synthesis. RESULTS: Of a total of 18,624 retrieved citations, we included 51 studies: 7 (14%) impact studies (answering the primary aim) and 44 (86%) correlational studies (answering the secondary aim). Impact studies reported mixed results with several limitations, including the use of interventions of inappropriately low intensity and short duration. The majority of correlational studies suggested a positive association between traditional bibliometrics and social media metrics (eg, number of mentions) in health research. CONCLUSIONS: We have identified suggestive yet inconclusive evidence on the impact of using social media to increase the number of citations in health research. Further studies with better design are needed to assess the causal link between social media impact and bibliometrics.

Phenotype Instance Verification and Evaluation Tool (PIVET): A Scaled Phenotype Evidence Generation Framework Using Web-Based Medical Literature.

BACKGROUND: Researchers are developing methods to automatically extract clinically relevant and useful patient characteristics from raw healthcare datasets. These characteristics, often capturing essential properties of patients with common medical conditions, are called computational phenotypes. Being generated by automated or semiautomated, data-driven methods, such potential phenotypes need to be validated as clinically meaningful (or not) before they are acceptable for use in decision making. OBJECTIVE: The objective of this study was to present Phenotype Instance Verification and Evaluation Tool (PIVET), a framework that uses co-occurrence analysis on an online corpus of publically available medical journal articles to build clinical relevance evidence sets for user-supplied phenotypes. PIVET adopts a conceptual framework similar to the pioneering prototype tool PheKnow-Cloud that was developed for the phenotype validation task. PIVET completely refactors each part of the PheKnow-Cloud pipeline to deliver vast improvements in speed without sacrificing the quality of the insights PheKnow-Cloud achieved. METHODS: PIVET leverages indexing in NoSQL databases to efficiently generate evidence sets. Specifically, PIVET uses a succinct representation of the phenotypes that corresponds to the index on the corpus database and an optimized co-occurrence algorithm inspired by the Aho-Corasick algorithm. We compare PIVET's phenotype representation with PheKnow-Cloud's by using PheKnow-Cloud's experimental setup. In PIVET's framework, we also introduce a statistical model trained on domain expert-verified phenotypes to automatically classify phenotypes as clinically relevant or not. Additionally, we show how the classification model can be used to examine user-supplied phenotypes in an online, rather than batch, manner. RESULTS: PIVET maintains the discriminative power of PheKnow-Cloud in terms of identifying clinically relevant phenotypes for the same corpus with which PheKnow-Cloud was originally developed, but PIVET's analysis is an order of magnitude faster than that of PheKnow-Cloud. Not only is PIVET much faster, it can be scaled to a larger corpus and still retain speed. We evaluated multiple classification models on top of the PIVET framework and found ridge regression to perform best, realizing an average F1 score of 0.91 when predicting clinically relevant phenotypes. CONCLUSIONS: Our study shows that PIVET improves on the most notable existing computational tool for phenotype validation in terms of speed and automation and is comparable in terms of accuracy.

The overview: an article that interrogates the literature.

Although the question on the efficacy of chlorhexidine therapy for the clinician's adult patient may not have been totally answered, a greater perspective on this treatment modality was gained by the reader because of the quality of the overview cited, and the evidence-based approach used by the authors in performing the overview.

Autoría en las publicaciones científicas - Criterios y conceptos

La definición de autoría en los artículos sobre investigación científica enfrenta serios problemas por la falta de criterios sobre quien puede y debe ser autor y sobre los reconocimientls a la particopación de personas diferentes a los autores en el trabajo de investigación. Muchos factores externos contribuyen a que los varios grupos de criterios que se han propuesto no sean ampliamente aceptados o cumplidos. Entre ellos la presión de publicación para promoción académica, la costumbre de retribuir con autoría a quien ha colaborado parcialmente en un estudio, la utilización del nombre de una o varias personas para facilitar la publicación y la falta de acuerdo inicial entre los investigadores sobre aspectos básicos de autoría. Se revisa la posición del Comité Internacional de Editores de Revistas Médicas y se hacen sugerencias para la adopción, adaptación y promoción de criterios de definición de autoría

Apreciación crítica de la literatura médica

El siguiente es el resumen del simposio que sobre el tema se llevó a cabo en el VII Congreso de Exalumnos de Medicina de la Universidad nacional de Colombia (AEXMUN). El artículo consta de tres partes : la primera introductoria escrita por G. Gómez sobre cómo y por qué evaluar críticamente la literatura biomédica, basado en los artículos y en el libro que sobre el tema han publicado docentes de la Universidad McMaster (1,2), la segunda sobre el ánalisis de exámenes diagnósticos por H. Gaitán, la tercera sobre análisis de estuduios sobre tratamientos, por R. Pardo.

A mathematical model of retrieval system performance.

There have been a number of major evaluations of the performance of retrieval systems against large full text and surrogate (bibliographic) databases. These evaluations have concentrated on the experimental determination of the Precision Ratio, the fraction of retrieved items that are relevant to an information request, and the Recall Ratio, the fraction of the total number of relevant items that were actually retrieved. While these measures have met with general acceptance, they have also generated much controversy. The purpose of this article is to review the results of several of the largest evaluations and to propose a simple model for the performance of such systems that may help explain the relationship between these measures and user behavior.

A comparison of manual and MEDLARS reviews of the literature on consultation-liaison psychiatry.

A systematic manual search for articles related to consultation-liaison psychiatry was compared to a computerized search of the same journals during the same period that was done with the Medical Literature Analysis and Retrieval System (MEDLARS). More articles were located with the manual method (94%) than with MEDLARS (65%).

Evaluating CD-ROM versions of the MEDLINE database: a checklist.

The emergence of CD-ROM (compact disc/read-only memory) versions of the MEDLINE database requires experienced MEDLINE searchers to examine assumptions about searching MEDLINE, since some expectations may not be fulfilled by this new technology. When applied to a particular CD-ROM MEDLINE product, the evaluation procedure involves testing assumptions concerning database contents; mechanics of searching; display, print, and download capabilities; and user-friendly features. The extent to which a CD-ROM product preserves and exploits important MEDLINE strengths should be assessed, e.g., the MeSH controlled vocabulary, the designation of major and minor MeSH emphasis, and the use of subheadings. Search software characteristics that affect ease of searching and quality of results also need to be examined, e.g., the ability to truncate search terms and the order of precedence in which Boolean operators are evaluated. A checklist to assist in the evaluation process is presented, including search examples for use in testing search functions.

NLM's practices for handling errata and retractions.

The keystone of the scientific method is solid experimental design and reproducible results. The publishing of findings advances knowledge and establishes the basis for further research. In recent years, the foundations of this principle have been shaken as a small, but significant portion of the scientific literature is being flawed by the appearance of fraudulently produced research. Potentially as damaging are errors that result from poor editing and proofreading. Fraudulent articles and errors lead, at best, to misunderstandings and, at worst, to dire consequences in the treatment of patients. Errata and retraction notices are generally carried in the published literature but usually are not linked to the original data. Database producers, such as NLM, have the means to establish this link and to inform users of incorrect information in the source documents as well. This paper reports NLM's experience in bringing published retraction and errata notices to the public's attention and relates this experience to the library's overall interest in quality assurance.

Descriptor and citation retrieval in the medical behavioral sciences literature: retrieval overlaps and novelty distribution.

Search results for nine topics in the Medical Behavioral Sciences are reanalyzed to compare the overall performance of descriptor and citation search strategies in identifying relevant and novel documents. Overlap percentages between an aggregate "descriptor-based" database (MEDLINE, EXCERPTA MEDICA, PSYCINFO) and an aggregate "citation-based" database (SCISEARCH, SOCIAL SCISEARCH) ranged from 1% to 26%, with a median overlap of 8% relevant retrievals found using both search strategies. For seven topics in which both descriptor and citation strategies produced reasonably substantial retrievals, two patterns of search performance and novelty distribution were observed: 1) Where descriptor and citation retrieval showed little overlap, novelty retrieval percentages differed by 17-23% between the two strategies; 2) Topics with a relatively high percentage retrieval overlap showed little difference (1-4%) in descriptor and citation novelty retrieval percentages. These results reflect the varying partial congruence of two literature networks and represent two different types of subject relevance.

Tests of methods for evaluating bibliographic databases: an analysis of the National Library of Medicine's handling of literatures in the medical behavioral sciences.

This article reports on five separate studies designed for the National Library of Medicine (NLM) to develop and test methodologies for evaluating the products of large databases. The methodologies were tested on literatures of the medical behavioral sciences (MBS). One of these studies examined how well NLM covered MBS monographic literature using CATLINE and OCLC. Another examined MBS journal and serial literature coverage in MEDLINE and other MBS-related databases available through DIALOG. These two studies used 1010 items derived from the reference lists of sixty-one journals, and tested for gaps and overlaps in coverage in the various databases. A third study examined the quality of the indexing NLM provides to MBS literatures and developed a measure of indexing as a system component. The final two studies explored how well MEDLINE retrieved documents on topics submitted by MBS professionals and how online searchers viewed MEDLINE (and other systems and databases) in handling MBS topics. The five studies yielded both broad research outcomes and specific recommendations to NLM.