Results 221 to 230 of about 212,878 (291)

Can Machine Learning Reduce Unnecessary Surgeries? A Retrospective Analysis Using Threshold Optimization to Prevent Negative Appendectomies in Adults

open access: yesAnnals of Gastroenterological Surgery, EarlyView.
Threshold‐optimized machine learning models using routine clinical and laboratory data in 623 adults undergoing appendectomy. Logistic regression (AUC = 0.765) and random forest (AUC = 0.785) were the best‐performing models for appendicitis detection and complicated appendicitis prediction, respectively.
Ivan Males   +8 more
wiley   +1 more source

FTIR Spectroscopy of Vitreous Humor for Postmortem Interval Estimation: A Multivariate Regression Approach. [PDF]

open access: yesInt J Mol Sci
Țurlea IR   +5 more
europepmc   +1 more source

Systematic Review and Meta‐Analysis on the Efficacy and Safety of Salvage Esophagectomy for T4 Esophageal Squamous Cell Carcinoma

open access: yesAnnals of Gastroenterological Surgery, EarlyView.
This meta‐analysis of 208 cases shows that salvage esophagectomy for cT4 esophageal squamous cell carcinoma achieves a 72% R0 resection rate, offering a curative pathway for selected patients. However, it remains a high‐risk procedure with an 18% anastomotic leak rate and 30% major complications (Clavien–Dindo ≥ III).
Makoto Sakai   +4 more
wiley   +1 more source

Multi-epigenome-wide analyses and meta-analysis of child maltreatment in judicial autopsies and intervened children and adolescents. [PDF]

open access: yesMol Psychiatry
Nishitani S   +20 more
europepmc   +1 more source

Innovations in Gastric Cancer Surgery During Early Minimally Invasive Era and Future Perspectives

open access: yesAnnals of Gastroenterological Surgery, EarlyView.
With continuing revelations in tumor biology and the emergence of artificial intelligence, new horizons for surgical innovation are opening. At the center of this transformative journey stands the innovative surgeon, driven by passion, guided by data, and steadfast in the commitment to patient safety and quality of life.
Reut El‐On, Young‐Woo Kim
wiley   +1 more source

AI in chemical engineering: From promise to practice

open access: yesAIChE Journal, EarlyView.
Abstract Artificial intelligence (AI) in chemical engineering has moved from promise to practice: physics‐aware (gray‐box) models are gaining traction, reinforcement learning complements model predictive control (MPC), and generative AI powers documentation, digitization, and safety workflows.
Jia Wei Chew   +4 more
wiley   +1 more source

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