1,720,962 research outputs found
Data Analisys approach to support Surgical Schedule Development: the case of A.O.R.N. “Antonio Cardarelli” of Naples (Italy)
No full textInfluencing Postoperative Length of Stay: Implications for Hospital Management in Head and Neck Cancer Care
No full textThis study explores the factors affecting the length of stay (LOS) in patients undergoing surgical treatment for head and neck cancer at Antonio Cardarelli Hospital between 2019 and 2022. Data from 492 patients were analyzed, focusing on variables such as age, gender, illness severity, mortality risk, and DRG weight. Descriptive statistics were computed, and a multiple linear regression model was used to identify predictors of postoperative LOS. The results showed that DRG weight was a significant determinant of postoperative LOS, reflecting the influence of procedural complexity on hospitalization duration. Other factors, including age, gender, urgency, and illness severity, did not show significant associations. These findings emphasize the importance of DRG classification in optimizing hospital workflows and resource management for head and neck cancer care, providing valuable insights for future research and clinical practice
Predicting Post-Operative Length of Stay after Robotic Urologic Surgery from Hospital Stay Characteristics: A Monocentric Study
No full textRobotic surgery has become a common approach in urological procedures, offering several advantages such as reduced operative time, fewer complications, and shorter hospital stays. However, some patients still experience complications and longer hospital stays, which lead to increased costs and patient discomfort. This monocentric study aimed to identify factors that predict post-operative length of stay (LOS) in male patients undergoing robotic surgery in the urology unit of Cardarelli Hospital in Naples, Italy, between 2020 and 2022. The factors analyzed included age, pre-operative LOS, severity of illness, and mortality risk. The study found that age and severity of illness were the most important predictors of longer post-operative LOS. The study recommends that physicians and healthcare providers should consider these factors when developing strategies to improve outcomes for patients undergoing robotic surgery
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Predicting Length of Stay in Colorectal Cancer Patients: A Monocentric Study in Italy
No full textColorectal cancer is associated with high healthcare costs, and reducing hospital length of stay (LOS) is a priority for hospitals. Our study aimed to predict LOS after surgical resection for colorectal cancer using demographic and clinical characteristics available in administrative data. We conducted a retrospective cohort study of patients discharged from Cardarelli Hospital, Naples, Italy, between 2019 and 2022. Multiple linear regression was used to model the relationship between demographic and clinical characteristics and LOS. The multiple regression model predicted postoperative LOS. Age and emergency department admission were significant predictors and positively correlated with postoperative LOS. Our findings suggest that age and emergency department admission may be useful predictors for identifying patients at risk for prolonged postoperative LOS after colorectal cancer surgery. These findings may inform resource allocation decisions in colorectal cancer surgery
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Precision and Management of Surgery Time Prediction: Comparative Analysis of ICD Codes, DRGs, and CCS Classifications in a Hospital Setting
No full textEfficient management of operating room (OR) time is critical to optimizing healthcare delivery. This study, conducted at Cardarelli Hospital, aimed to determine the most reliable predictor of OR time by comparing International Classification of Diseases (ICD) codes, Diagnosis-Related Groups (DRGs), and Clinical Classification Software (CCS). Accuracy was measured by coefficient of variation (CV). Analysis of data from 19,430 procedures over 12 months, each paired with the classification system and surgical team involved, revealed varying levels of precision within different CV classes. For the ICD9-CM team, 3.4% of procedures had a CV < 0.25 and 59.7% had a CV < 0.5. CCS Level 1 team showed 0.1% and 12.1% for CV < 0.25 and CV < 0.5, respectively. The CCS Single Level team had 1.1% and 44.4%, and the DRG team had 0.9% and 41.5% for CV < 0.25 and CV < 0.5, respectively. ICD codes showed practical precision, while CCS classifications showed increased variability. DRGs provided a balance between simplicity and accuracy. Limitations include concerns about data quality, generalizability, and the impact of human factors in coding. These findings provide nuanced insights into predicting operative time to improve OR efficiency. Future studies should address these limitations and explore additional predictors for comprehensive analyses
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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