RESUMEN
Physicians could greatly benefit from automated diagnosis and prognosis tools to help address information overload and decision fatigue. Intensive care physicians stand to benefit greatly from such tools as they are at particularly high risk for those factors. Acute Respiratory Distress Syndrome (ARDS) is a life-threatening condition affecting >10% of critical care patients and has a mortality rate over 40%. However, recognition rates for ARDS have been shown to be low (30-70%) in clinical settings. In this work, we present a reproducible computational pipeline that automatically adjudicates ARDS on retrospective datasets of mechanically ventilated adult patients. This pipeline automates the steps outlined by the Berlin Definition through implementation of natural language processing tools and classification algorithms. We train an XGBoost model on chest imaging reports to detect bilateral infiltrates, and another on a subset of attending physician notes labeled for the most common ARDS risk factor in our data. Both models achieve high performance-a minimum area under the receiver operating characteristic curve (AUROC) of 0.86 for adjudicating chest imaging reports in out-of-bag test sets, and an out-of-bag AUROC of 0.85 for detecting a diagnosis of pneumonia. We validate the entire pipeline on a cohort of MIMIC-III encounters and find a sensitivity of 93.5% - an extraordinary improvement over the 22.6% ARDS recognition rate reported for these encounters - along with a specificity of 73.9%. We conclude that our reproducible, automated diagnostic pipeline exhibits promising accuracy, generalizability, and probability calibration, thus providing a valuable resource for physicians aiming to enhance ARDS diagnosis and treatment strategies. We surmise that proper implementation of the pipeline has the potential to aid clinical practice by facilitating the recognition of ARDS cases at scale.
RESUMEN
Female representation has been slowly but steadily increasing in many sectors of society. One sector where one would expect to see gender parity is the movie industry, yet the representation of females in most functions within the U.S. movie industry remain surprisingly low. Here, we study the historical patterns of female representation among actors, directors, and producers in an attempt to gain insights into the possible causes of the lack of gender parity in the industry. Our analyses reveals a remarkable temporal coincidence between the collapse in female representation across all functions and the advent of the Studio System, a period when the major Hollywood studios controlled all aspects of the industry. Female representation among actors, directors, producers and writers dropped to extraordinarily low values during the emergence and consolidation of the Studio System that in some cases have not yet recovered to pre-Studio System levels. In order to explore some possible mechanisms behind these patterns, we investigate the association between the gender balance of actors, writers, directors, and producers and a number of economic indicators, movie industry indicators, and movie characteristics. We find robust, strong, and significant associations which are consistent with an important role for the gender of decision makers on the gender balance of other industry functions. While in no way demonstrating causality, our findings add new perspectives to the discussions of the reasons for female under-representation in fields such as computer science and medicine, that have also experienced dramatic changes in female representation.