A comparison between ilioinguinal and modified Stoppa approach in anterior column acetabular fractures

Introduction: Pelvis fractures are among the most difficult fractures to manage and treat for Orthopedic surgeons. Anatomic reduction is the main goal to reach in the acetabular fractures' treatment. The following study compares clinical outcomes and complications of Ilioinguinal versus modified Stoppa approach in Open Reduction and Internal Fixation (ORIF) of anterior column acetabulum fractures. Materials and methods: A comparative analysis on 90 patients undergoing ORIF on acetabular fracture has been performed. Patients have been divided into two groups. The first group was treated by Ilioinguinal approach (n = 48), the second group by modified Stoppa approach (n = 42). The following parameters have been compareted: quality of fragment reduction; operative time; peri‐ and post-operative blood loss; complications; clinical and radiographic outcomes. Results: The modified Stoppa approach has shown a shorter mean operative time (146 min vs 175 min), fewer complications (14/48 vs 6/42), less blood loss both in the perio-operative phase (0.8 Hb pt vs 1.3 Hb pt) than in postoperative one (1.1 Hb pt vs 1.5 Hb pt), a lower rate of nerve, infections and critical complications. On the other hand, the ilioinguinal approach has showed better results in terms of quality of fracture reduction (43/48 patiens with anatomical or near anatomical reduction vs. 37/42). No significant differences concerning vascular lesions, clinical and functional outcomes have been found between the two groups. Conclusions: The modified Stoppa approach results in shorter operative time, less intra-operative blood loss and fewer complications than the ilioinguinal one. Greater anatomic reduction is achieved by Ilioinguinal approach; however, this does not necessarily translate into better clinical and functional outcomes which, overall, are comparable in the two analysed approaches. In conclusion, the modified Stoppa approach is deemed to be a better alternative in treating these fractures

Development of natural language processing tools to support determination of federal disability benefits in the U.S.

The disability benefits programs administered by the US Social Security Administration (SSA) receive between 2 and 3 million new applications each year. Adjudicators manually review hundreds of evidence pages per case to determine eligibility based on financial, medical, and functional criteria. Natural Language Processing (NLP) technology is uniquely suited to support this adjudication work and is a critical component of an ongoing inter-agency collaboration between SSA and the National Institutes of Health. This NLP work provides resources and models for document ranking, named entity recognition, and terminology extraction in order to automatically identify documents and reports pertinent to a case, and to allow adjudicators to search for and locate desired information quickly. In this paper, we describe our vision for how NLP can impact SSA’s adjudication process, present the resources and models that have been developed, and discuss some of the benefits and challenges in working with large-scale government data, and its specific properties in the functional domain

Linking free text documentation of functioning and disability to the ICF with natural language processing

Background: Invaluable information on patient functioning and the complex interactions that define it is recorded in free text portions of the Electronic Health Record (EHR). Leveraging this information to improve clinical decision-making and conduct research requires natural language processing (NLP) technologies to identify and organize the information recorded in clinical documentation. Methods: We used natural language processing methods to analyze information about patient functioning recorded in two collections of clinical documents pertaining to claims for federal disability benefits from the U.S. Social Security Administration (SSA). We grounded our analysis in the International Classification of Functioning, Disability, and Health (ICF), and used the Activities and Participation domain of the ICF to classify information about functioning in three key areas: mobility, self-care, and domestic life. After annotating functional status information in our datasets through expert clinical review, we trained machine learning-based NLP models to automatically assign ICF categories to mentions of functional activity. Results: We found that rich and diverse information on patient functioning was documented in the free text records. Annotation of 289 documents for Mobility information yielded 2,455 mentions of Mobility activities and 3,176 specific actions corresponding to 13 ICF-based categories. Annotation of 329 documents for Self-Care and Domestic Life information yielded 3,990 activity mentions and 4,665 specific actions corresponding to 16 ICF-based categories. NLP systems for automated ICF coding achieved over 80% macro-averaged F-measure on both datasets, indicating strong performance across all ICF categories used. Conclusions: Natural language processing can help to navigate the tradeoff between flexible and expressive clinical documentation of functioning and standardizable data for comparability and learning. The ICF has practical limitations for classifying functional status information in clinical documentation but presents a valuable framework for organizing the information recorded in health records about patient functioning. This study advances the development of robust, ICF-based NLP technologies to analyze information on patient functioning and has significant implications for NLP-powered analysis of functional status information in disability benefits management, clinical care, and research

Broadening horizons: the case for capturing function and the role of health informatics in its use

Background Human activity and the interaction between health conditions and activity is a critical part of understanding the overall function of individuals. The World Health Organization’s International Classification of Functioning, Disability and Health (ICF) models function as all aspects of an individual’s interaction with the world, including organismal concepts such as individual body structures, functions, and pathologies, as well as the outcomes of the individual’s interaction with their environment, referred to as activity and participation. Function, particularly activity and participation outcomes, is an important indicator of health at both the level of an individual and the population level, as it is highly correlated with quality of life and a critical component of identifying resource needs. Since it reflects the cumulative impact of health conditions on individuals and is not disease specific, its use as a health indicator helps to address major barriers to holistic, patient-centered care that result from multiple, and often competing, disease specific interventions. While the need for better information on function has been widely endorsed, this has not translated into its routine incorporation into modern health systems. Purpose We present the importance of capturing information on activity as a core component of modern health systems and identify specific steps and analytic methods that can be used to make it more available to utilize in improving patient care. We identify challenges in the use of activity and participation information, such as a lack of consistent documentation and diversity of data specificity and representation across providers, health systems, and national surveys. We describe how activity and participation information can be more effectively captured, and how health informatics methodologies, including natural language processing (NLP), can enable automatically locating, extracting, and organizing this information on a large scale, supporting standardization and utilization with minimal additional provider burden. We examine the analytic requirements and potential challenges of capturing this information with informatics, and describe how data-driven techniques can combine with common standards and documentation practices to make activity and participation information standardized and accessible for improving patient care. Recommendations We recommend four specific actions to improve the capture and analysis of activity and participation information throughout the continuum of care: (1) make activity and participation annotation standards and datasets available to the broader research community; (2) define common research problems in automatically processing activity and participation information; (3) develop robust, machine-readable ontologies for function that describe the components of activity and participation information and their relationships; and (4) establish standards for how and when to document activity and participation status during clinical encounters. We further provide specific short-term goals to make significant progress in each of these areas within a reasonable time frame

Inductive identification of functional status information and establishing a gold standard corpus: a case study on the mobility domain

The importance of functional status information (FSI) has become increasingly evident in recent years [1, 2]. However, implementation, application, and normalization of FSI in health care and Electronic Health Records (EHRs) have been largely underexplored. The World Health Organization's International Classification of Functioning, Disability and Health (ICF) [3] is considered to be the international standard for describing and coding function and health states. Nevertheless, the ICF provides only a limited vocabulary for recognizing FSI descriptions, since its purpose is to organize concepts related to functioning rather than to provide a comprehensive terminology or a complete set of relations between concepts. While the free text portion of EHRs might provide a more complete picture of health status, treatment, and progress, current Natural Language Processing (NLP) methods largely focus on extracting medical conditions (e.g. diagnoses and symptoms, etc.). The absence of a standardized functional terminology and incompleteness of the ICF as a vocabulary source makes it challenging to build a NLP system to extract FSI from EHR free text. Our work takes the first step towards extraction of FSI from free text by systematically identifying the structure of FSI related to Mobility, a key domain of the ICF and an important domain in the determination of work disability. Our interdisciplinary research group inductively evaluated examples extracted from over 1,200 Physical Therapy (PT) notes from the Clinical Center of the National Institutes of Health (NIH). This extensive work resulted in a nested entity structure comprised of 2 entities, 3 sub-entities, 8 attributes, and 21 attribute values. Furthermore, we have manually curated the first gold standard corpus of 200 double-annotated and 50 triple-annotated PT notes. Our inter-annotator agreement (IAA) averages 97% F1-score on partial textual span matching and from 0.4 to 0.9 Siegel & Castellan's kappa on attribute value matching. Such a rich semantic corpus of Mobility FSI is valuable and a promising resource for future statistical learning. Our method is also adaptable to other domains of the ICF

SARS-CoV-2 infection among hospitalised pregnant women and impact of different viral strains on COVID-19 severity in Italy: a national prospective population-based cohort study

OBJECTIVE: The primary aim of this article was to describe SARS-CoV-2 infection among pregnant women during the wild-type and Alpha-variant periods in Italy. The secondary aim was to compare the impact of the virus variants on the severity of maternal and perinatal outcomes. DESIGN: National population-based prospective cohort study. SETTING: A total of 315 Italian maternity hospitals. SAMPLE: A cohort of 3306 women with SARS-CoV-2 infection confirmed within 7 days of hospital admission. METHODS: Cases were prospectively reported by trained clinicians for each participating maternity unit. Data were described by univariate and multivariate analyses. MAIN OUTCOME MEASURES: COVID-19 pneumonia, ventilatory support, intensive care unit (ICU) admission, mode of delivery, preterm birth, stillbirth, and maternal and neonatal mortality. RESULTS: We found that 64.3% of the cohort was asymptomatic, 12.8% developed COVID-19 pneumonia and 3.3% required ventilatory support and/or ICU admission. Maternal age of 30-34 years (OR 1.43, 95% CI 1.09-1.87) and ≥35 years (OR 1.62, 95% CI 1.23-2.13), citizenship of countries with high migration pressure (OR 1.75, 95% CI 1.36-2.25), previous comorbidities (OR 1.49, 95% CI 1.13-1.98) and obesity (OR 1.72, 95% CI 1.29-2.27) were all associated with a higher occurrence of pneumonia. The preterm birth rate was 11.1%. In comparison with the pre-pandemic period, stillbirths and maternal and neonatal deaths remained stable. The need for ventilatory support and/or ICU admission among women with pneumonia increased during the Alpha-variant period compared with the wild-type period (OR 3.24, 95% CI 1.99-5.28). CONCLUSIONS: Our results are consistent with a low risk of severe COVID-19 disease among pregnant women and with rare adverse perinatal outcomes. During the Alpha-variant period there was a significant increase of severe COVID-19 illness. Further research is needed to describe the impact of different SARS-CoV-2 viral strains on maternal and perinatal outcomes

Recent advances in pore water pressure and liquefaction characteristics of low plasticity silty sands subjected to cyclic loading

Abstract Low plasticity sand-silt mixtures are very common in Italy either in natural depositional environment or in man-made earth-fill, hence the knowledge of their behavior is a crucial aspect in many practical applications. Due to higher compressibility features, significant strains and strength loss may be triggered by earthquakes. The results of a laboratory-based investigation undertaken on undisturbed samples of low plasticity silty-sandy soils recovered from a bank stretch after the 2012 Emilia Romagna earthquake in Italy, when serious damages and widespread liquefaction events were observed, are herein presented. Special emphasis was given to susceptibility to liquefaction and pore water pressure response in presence of an initial static shear stress. As part of the present work, the results of undrained cyclic simple shear (CSS) tests carried out on reconstituted specimens of sand-fines mixtures, covering a range of non plastic fines contents from 0% to 40%, were used for predicting undrained cyclic resistance through the concept of equivalent granular void ratio, e* . The conceptual framework based on e* appears appropriate for streamlining the effect of fines on cyclic liquefaction resistance of these intermediate soils, provided that fines content is less than a limiting value. Since an important stage in the assessment of liquefaction potential is to predict excess pore water pressure during cyclic loading, the results of CSS tests were also utilized for analysing pore water pressure generation models of silty sands over a wide range of fines contents.</jats:p