Results 131 to 140 of about 611 (222)

Short‐Term Outcomes and Cost Drivers of Emergency Surgery for Acute Abdominal Disease in Super‐Elderly Patients: A Study in the Japanese Tertiary Care Hospital

Annals of Gastroenterological Surgery, EarlyView.
This retrospective study analyzed patients aged ≥ 85 years undergoing emergency abdominal surgery, focusing on short‐term outcomes and inpatient cost structure under the Japanese DPC system. Although major complications occurred in 19.4% of patients, more than 70% were discharged home.
Yuta Kobayashi, Ryo Oikawa, Yumi Shibuya, Masakazu Tatsuno, Atsuya Kamiyama, Tsuyoshi Ozawa, Keiichiro Hara, Shojiro Hata, Hirokazu Yamaguchi   +8 more
wiley   +1 more source

Characteristics of Anal Fistula Cancer in Patients Who Underwent Surgery: A Nationwide Observational Study Using Diagnosis Procedure Combination Data

Annals of Gastroenterological Surgery, EarlyView.
The characteristics of patients with anal fistula cancer included a higher proportion of men, younger age, more smokers, and tumor‐related characteristics, including more advanced local progression. Surgical characteristics included a lower rate of minimally invasive surgery, a higher proportion of total pelvic exenterations, and longer postoperative ...
Nobuaki Hoshino, Hiromitsu Kinoshita, Kazutaka Obama, Kiyohide Fushimi, Susumu Kunisawa, Yuichi Imanaka   +5 more
wiley   +1 more source

Counterbalance Economics (CBE): Harnessing Aggregate Markups to Finance Voucher-Based Inequality Reduction. [PDF]

F1000Res
Malliaros P, Pacheco-Jaramillo WA.
europepmc   +1 more source

Relationship Between Hospital Volume and Outcomes of Minimally Invasive Esophagectomy for Esophageal Cancer: Analysis of the National Clinical Database in Japan

Annals of Gastroenterological Surgery, EarlyView.
According to multivariable analysis conducted using a hierarchical logistic regression model, using VHH as the reference group, the odds ratios for mortality by hospital volume category were as follows: VLH, 2.70 (p < 0.0001); LH, 1.72 (p = 0.052); MH, 1.70 (p = 0.034); and HH, 1.43 (p = 0.173).
Soji Ozawa, Hiraku Kumamaru, Yuko Kitagawa, Kazuo Koyanagi, Junya Oguma, Harushi Udagawa, Hiroaki Miyata, Yasushi Toh, Hisahiro Matsubara   +8 more
wiley   +1 more source

Factors Influencing Non-urgent Emergency Department Visits: A Review of the Causes and Impact. [PDF]

Cureus
Alayob MA, Azeem SK, Ali S, Al Farras M.
europepmc   +1 more source

Taguchi–Bayesian Sampling: A Roadmap for Polymer Database Construction Toward Small Representative Machine Learning

Advanced Intelligent Discovery, EarlyView.
This article establishes a Taguchi–Bayesian sampling strategy to reconstruct polymer processing–property landscape at minimal sampling cost, generically building the roadmap for materials database construction from sampling their vast design space. This sampling strategy is featured by an alternating lesson between uniformity and representativeness ...
Han Liu, Liantang Li
wiley   +1 more source

Wearables in Healthcare Organizations: Implications for Occupational Health, Organizational Performance, and Economic Outcomes. [PDF]

Healthcare (Basel)
Virgillito D, Catalfo P, Ledda C.
europepmc   +1 more source

“Index-Numbers” and Wages-Regulation [PDF]

Nature, 1921
openaire   +1 more source

LLM‐Based Scientific Assistants for Knowledge Extraction: Which Design Choices Matter?

Advanced Intelligent Discovery, EarlyView.
A comprehensive framework for optimizing Large Language Models in domain‐specific applications is introduced. The LLM Playground integrates Prompt Engineering, knowledge augmentation, and advanced reasoning strategies to enable systematic comparison of architectures and base models.
David Exler, Manuel Raimann, Marc Münker, Maxim Rosin, Joaquin E. Urrutia Gómez, Christof M. Niemeyer, Markus Reischl, Luca Rettenberger   +7 more
wiley   +1 more source

Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties With Phonon‐Informed Datasets

Advanced Intelligent Discovery, EarlyView.
Phonons‐informed machine‐learning predictive models are propitious for reproducing thermal effects in computational materials science studies. Machine learning (ML) methods have become powerful tools for predicting material properties with near first‐principles accuracy and vastly reduced computational cost.
Pol Benítez, Cibrán López, Edgardo Saucedo, Teruyasu Mizoguchi, Claudio Cazorla   +4 more
wiley   +1 more source