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SIGAPS score determines to a great extent each university hospital's funding for research. This score is considered to reflect the scientific production. Due to its financial impact, he may modify authors' attitudes leading them to publish their articles in French. Our objective was to evaluate the association between the SIGAPS score and publications in French.Based on Rouen University Hospital's (RUH) SIGAPS data from 2007 to 2010, we used Spearman's correlation to establish an association between SIGAPS score of each author and the fact that they published in French.A positive correlation was found between SIGAPS score and author's number of publications in French (r(s)=0.51; IC(95%)=[0.44; 0.58]). The relationship between the SIGAPS score and the author's publication rate in French was negative but higher (r(s)=-0.87; IC(95%)=[-1.0; -0.68]; author's deciles).The relationship between the SIGAPS score and the number of publications in French is not surprising as the SIGAPS score is based on the number of publications. As regards to the publication rate in French, this was even more interesting as our results showed that the more productive an author was, the less they published their results in French. Publications in French did not appear to be the best way to improve individual SIGAPS score. There is high heterogeneity between authors who are very prolific, with one third having a publication rate in French high above the average and one third who had a publication rate in French well below the average.There was a high negative correlation between SIGAPS score and the publication rate in French for RUH's researchers (author's deciles).

CCAM is a French terminology for coding clinical procedures. CCAM is a multi-hierarchical structured classification for procedures used in France for reimbursement in health care, which is external to UMLS.The objective of this work is to describe a French lexical approach allowing mapping CCAM procedures to the UMLS Metathesaurus to achieve interoperability to multiple international terminologies. This approach used a preliminary step intended to take only the significant characters used to code CCAM corresponding to anatomical and actions axes.According to the 7,926 CCAM codes used in this study, 5,212 possible matches (exact matching, single to multiple matching, partial matching) are found using the French CCAM to UMLS based mapping, 65% of the corresponding anatomical terms in the CCAM code are mapped to at least one UMLS Concept and 37% of the corresponding action terms in the CCAM code are mapped to at least one UMLS Concept. For all the exact matches found (n=200), 91% were rated by a human expert as narrower than the mapped UMLS Concepts, while only 3% were irrelevant.

SIGAPS is a bibliometric score that aims at making an inventory, evaluating and promoting scientific publications of hospitals that perform research. It has become a major stake in France since it is one of the most important components of the MERRI (Mission Training, Research, Reference and Innovation) founding of hospitals. This score is based on the points attributed to the authors of articles published in journals indexed in Medline, according to the rank of the authors and the Impact Factor of the journal.to compare the reliability of the score when applying different way of computing it, and different weights for the rank or the Impact Factor.we computed the scores of all the physicians of a University Hospital, using the rules that are actually applied at the national level. We then used 4 different scenarios, with different weight given to the rank of authors or the Impact Factor. We compared the scores obtained by each author according to the different scenarios with the Spearman's rank and Pearson's correlation coefficients.The score is not significantly affected when no points are given to the fourth authors and above, when the last author get more points or to change the points according to the Impact Factor of the journal.The different scenarios do not lead to significant changes for the physicians' scores, and therefore for the cumulated score of the hospital. Despite the well known limits of bibliometric indicators, the SIGAPS score appears reliable to compare the hospitals for founding decisions.

The aim of this study is to provide to indexers MeSH terms to be considered as major ones in a list of terms automatically extracted from a document.We propose a method combining symbolic knowledge - the UMLS Metathesaurus and Semantic Network - and statistical knowledge drawn from co-occurrences of terms in the CISMeF database (a French-language quality-controlled health gateway) using data mining measures. The method was tested on CISMeF corpus of 293 resources.There was a proportion of 0.37+/-0.26 major terms in the processed records. The method produced lists of terms with a proportion of terms initially pointed out as major of 0.54+/-0.31.The method we propose reduces the number of terms, which seem not useful for content description of resources, such as "check tags", but retains the most descriptive ones. Discarding these terms is accounted for by: 1) the removal by using semantic knowledge of associations of concepts bearing no real medical significance, 2) the removal by using statistical knowledge of nonstatistically significant associations of terms.This method can assist effectively indexers in their daily work and will be soon applied in the CISMeF system.

French pharmaceutical theses are rarely quoted. If the main obstacles originate from language or access barriers, proper indexation could also be blamed. Manually extracted key-words don't necessary come from a structured thesaurus. In the following work, this manual indexing method is compared to an automated one, "Nomindex", based on UMLS. The automated method is improved by the addition of a relevance scoring system. The first indexing step consists of downloading, adapting and indexing theses in electronic format. Results will then be analyzed and sorted by relevance, through the comparison of classic statistical indices (noise, silence and relevance). It was assumed that the manually obtained key-words were always relevant. The silence of manual indexing is nevertheless high: seven new key-words are proposed by Nomindex, which results are mixed (10% of silence, but 50% of noise). These results are promising on the first experiment on pharmaceutical document without lexicon improvement. The indexing, if it is currently insufficient for a real life use, could easily be improved by specific updates of the lexicon.