TY  - JOUR
A1  - Oliveira-Ciabati, Livia
A1  - Loures dos Santos, Luciane
A1  - Hsiou Schmaltz, Annie
A1  - Sasso, Ariane Morassi
A1  - Castro, Margaret de
A1  - Souza, João Paulo
T1  - Scientific sexism
BT  - the gender bias in the scientific production of the Universidade de São Paulo
JF  - Revista de saúde pública : publication of the Faculdade de Saúde Pública da Universidade de São Paulo = Journal of public health
N2  - OBJECTIVE: 
To investigate gender inequity in the scientific production of the University of Sao Paulo. 

METHODS: 
Members of the University of Sao Paulo faculty are the study population. The Web of Science repository was the source of the publication metrics. We selected the measures: total publications and citations, average of citations per year and item, H-index, and history of citations between 1950 and 2019. We used the name of the faculty member as a proxy to the gender identity. We use descriptive statistics to characterize the metrics. We evaluated the scissors effect by selecting faculty members with a high H-index. The historical series of citations was projected until 2100. We carry out analyses for the general population and working time subgroups: less than 10 years, 10 to 20 years, and 20 years or more. 

RESULTS: 
Of the 8,325 faculty members, we included 3,067 (36.8%). Among those included, 1,893 (61.7%) were male and 1,174 (38.28%) female. The male gender presented higher values in the publication metrics (average of articles: M = 67.0 versus F = 49.7; average of citations/year: M = 53.9 versus F = 35.9), and H-index (M = 14.5 versus F = 12.4). Among the 100 individuals with the highest H-index (>= 37), 83% are male. The male curve grows faster in the historical series of citations, opening a difference between the groups whose separation is confirmed by the projection. 


DISCUSSION:
Scientific production at the Universidade de Sao Paulo is subject to a gender bias. Two-thirds of the faculty are male, and hiring over the past few decades perpetuates this pattern. The large majority of high impact faculty members are male.

CONCLUSION: 
Our analysis suggests that the Universidade de Sao Paulo will not overcome gender inequality in scientific production without substantive affirmative action. Development does not happen by chance but through choices that are affirmative, decisive, and long-term oriented.
KW  - Sexism
KW  - Scientific Publication Indicators
KW  - Gender Inequality
Y1  - 2021
U6  - https://doi.org/10.11606/s1518-8787.2021055002939
SN  - 1518-8787
VL  - 55
PB  - Faculdade de Saúde Pública da Universidade de São Paulo
CY  - São Paulo
ER  - 
TY  - JOUR
A1  - Datta, Suparno
A1  - Sachs, Jan Philipp
A1  - Freitas da Cruz, Harry
A1  - Martensen, Tom
A1  - Bode, Philipp
A1  - Morassi Sasso, Ariane
A1  - Glicksberg, Benjamin S.
A1  - Böttinger, Erwin
T1  - FIBER
BT  - enabling flexible retrieval of electronic health records data for clinical predictive modeling
JF  - JAMIA open
N2  - Objectives: 
The development of clinical predictive models hinges upon the availability of comprehensive clinical data. Tapping into such resources requires considerable effort from clinicians, data scientists, and engineers. Specifically, these efforts are focused on data extraction and preprocessing steps required prior to modeling, including complex database queries. A handful of software libraries exist that can reduce this complexity by building upon data standards. However, a gap remains concerning electronic health records (EHRs) stored in star schema clinical data warehouses, an approach often adopted in practice. In this article, we introduce the FlexIBle EHR Retrieval (FIBER) tool: a Python library built on top of a star schema (i2b2) clinical data warehouse that enables flexible generation of modeling-ready cohorts as data frames. 

Materials and Methods: 
FIBER was developed on top of a large-scale star schema EHR database which contains data from 8 million patients and over 120 million encounters. To illustrate FIBER's capabilities, we present its application by building a heart surgery patient cohort with subsequent prediction of acute kidney injury (AKI) with various machine learning models. 

Results:
Using FIBER, we were able to build the heart surgery cohort (n = 12 061), identify the patients that developed AKI (n = 1005), and automatically extract relevant features (n = 774). Finally, we trained machine learning models that achieved area under the curve values of up to 0.77 for this exemplary use case.

Conclusion: 
FIBER is an open-source Python library developed for extracting information from star schema clinical data warehouses and reduces time-to-modeling, helping to streamline the clinical modeling process.
KW  - databases
KW  - factual
KW  - electronic health records
KW  - information storage and
KW  - retrieval
KW  - workflow
KW  - software/instrumentation
Y1  - 2021
U6  - https://doi.org/10.1093/jamiaopen/ooab048
SN  - 2574-2531
VL  - 4
IS  - 3
PB  - Oxford Univ. Press
CY  - Oxford
ER  -