Dataset Information

GenderMedDB: an interactive database of sex and gender-specific medical literature.

ABSTRACT:

Background

Searches for sex and gender-specific publications are complicated by the absence of a specific algorithm within search engines and by the lack of adequate archives to collect the retrieved results. We previously addressed this issue by initiating the first systematic archive of medical literature containing sex and/or gender-specific analyses. This initial collection has now been greatly enlarged and re-organized as a free user-friendly database with multiple functions: GenderMedDB (http://gendermeddb.charite.de).

Description

GenderMedDB retrieves the included publications from the PubMed database. Manuscripts containing sex and/or gender-specific analysis are continuously screened and the relevant findings organized systematically into disciplines and diseases. Publications are furthermore classified by research type, subject and participant numbers. More than 11,000 abstracts are currently included in the database, after screening more than 40,000 publications. The main functions of the database include searches by publication data or content analysis based on pre-defined classifications. In addition, registrants are enabled to upload relevant publications, access descriptive publication statistics and interact in an open user forum.

Conclusions

Overall, GenderMedDB offers the advantages of a discipline-specific search engine as well as the functions of a participative tool for the gender medicine community.

SUBMITTER: Oertelt-Prigione S

PROVIDER: S-EPMC4047004 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

GenderMedDB: an interactive database of sex and gender-specific medical literature.

Oertelt-Prigione Sabine S Gohlke Björn-Oliver BO Dunkel Mathias M Preissner Robert R Regitz-Zagrosek Vera V

Biology of sex differences 20140523

<h4>Background</h4>Searches for sex and gender-specific publications are complicated by the absence of a specific algorithm within search engines and by the lack of adequate archives to collect the retrieved results. We previously addressed this issue by initiating the first systematic archive of medical literature containing sex and/or gender-specific analyses. This initial collection has now been greatly enlarged and re-organized as a free user-friendly database with multiple functions: Gender ...[more]

PMID: 24904731

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Project description:BackgroundEmergency medicine (EM) residents do not generally receive sex- and gender-specific education. There will be increasing attention to this gap as undergraduate medical education integrates it within their curriculum.MethodologyMembers of the Sex and Gender in Emergency Medicine (SGEM) Interest Group set out to develop a SGEM toolkit and pilot integrating developed components at multiple residency sites. The curriculum initiative involved a pre- and posttraining assessment that included basic demographics and queries regarding previous training in sex-/gender-based medicine (SGBM). It was administered to PGY-1 to -4 residents who participated in a 3-hour training session that included one small group case-based discussion, two oral board cases, and one simulation and group debriefing.AnalysisComponents of the developed toolkit (https://www.sexandgenderhealth.org) were implemented at four unique SGEM Interest Group member residency programs. Residents (n = 82/174, 47%) participated; 64% (n = 49) were male and 36% (n = 28) were female. Twenty-six percent (n = 21) of the residents reported that they had less than 1 hour of training in this domain during residency; 59% (n = 48) reported they had 1 to 6 hours and 16% (n = 13) reported they had >6 hours. The average preassessment score was 61% and postassessment was 88%. After training, 74% (n = 60) felt that their current practice would have benefited from further training in sex-/gender-based topics in medicine during medical school and 83% (n = 67) felt their clinical practice would have benefited from further training in this domain during residency.ImplicationsThe majority of EM residents who participated in this training program reported that they had limited instruction in this domain in medical school or residency. This initiative demonstrated a method that can be emulated for the incorporation of SGBM educational components into an EM residency training educational day. After training, the majority of residents who participated felt that their current practice would have benefited from further training in sex- and gender-based topics in residency.

Project description:BackgroundAdvances in the next generation sequencing technology has accelerated the pace of individualized medicine (IM), which aims to incorporate genetic/genomic information into medicine. One immediate need in interpreting sequencing data is the assembly of information about genetic variants and their corresponding associations with other entities (e.g., diseases or medications). Even with dedicated effort to capture such information in biological databases, much of this information remains 'locked' in the unstructured text of biomedical publications. There is a substantial lag between the publication and the subsequent abstraction of such information into databases. Multiple text mining systems have been developed, but most of them focus on the sentence level association extraction with performance evaluation based on gold standard text annotations specifically prepared for text mining systems.ResultsWe developed and evaluated a text mining system, MutD, which extracts protein mutation-disease associations from MEDLINE abstracts by incorporating discourse level analysis, using a benchmark data set extracted from curated database records. MutD achieves an F-measure of 64.3% for reconstructing protein mutation disease associations in curated database records. Discourse level analysis component of MutD contributed to a gain of more than 10% in F-measure when compared against the sentence level association extraction. Our error analysis indicates that 23 of the 64 precision errors are true associations that were not captured by database curators and 68 of the 113 recall errors are caused by the absence of associated disease entities in the abstract. After adjusting for the defects in the curated database, the revised F-measure of MutD in association detection reaches 81.5%.ConclusionsOur quantitative analysis reveals that MutD can effectively extract protein mutation disease associations when benchmarking based on curated database records. The analysis also demonstrates that incorporating discourse level analysis significantly improved the performance of extracting the protein-mutation-disease association. Future work includes the extension of MutD for full text articles.

Dataset Information

GenderMedDB: an interactive database of sex and gender-specific medical literature.

Background

Description

Conclusions

Publications

GenderMedDB: an interactive database of sex and gender-specific medical literature.

Similar Datasets

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