Results 201 to 210 of about 132,160 (305)

Navigating the Real World: A Scoping Review of Structured Frameworks to Effectively Identify, Evaluate, and Select Real‐World Data Sources for Fit‐for‐Purpose Studies

open access: yesClinical Pharmacology &Therapeutics, EarlyView.
The potential of real‐world data (RWD), particularly from patient registries, has been increasingly recognized over the last decade by academia, regulators, and health technology assessment (HTA) bodies for its role in assessing a product's effectiveness and supporting regulatory submissions.
Sonia Zebachi   +13 more
wiley   +1 more source

From Tweets to Insights: Social Opinion Mining on Corporate Social Responsibility

open access: yesCorporate Social Responsibility and Environmental Management, EarlyView.
ABSTRACT Corporate Social Responsibility (CSR) has become increasingly critical as firms seek to balance financial goals with social and environmental responsibilities. Our study introduces a three‐phase structured method to analyze stakeholders' opinions on CSR through Social Opinion Mining, utilizing stakeholder and legitimacy theories.
Chiara Leggerini, Mariasole Bannò
wiley   +1 more source

FAIR assessment of Disease Maps fosters open science and scientific crowdsourcing in systems biomedicine. [PDF]

open access: yesSci Data
Balaur I   +9 more
europepmc   +1 more source

Internet of Things (IoT) and the Environmental Sustainability: A Literature Review and Recommendations for Future Research

open access: yesCorporate Social Responsibility and Environmental Management, EarlyView.
ABSTRACT As the Internet of Things (IoT) surges forward, intersecting with an urgent demand for environmental sustainability (ES), digital technologies emerge as potent orchestrators of systemic transformation. Employing a subtle blend of bibliometric‐systematic literature review (B‐SLR) and qualitative insights, this research investigates IoT's ...
Giuseppe Lanfranchi   +2 more
wiley   +1 more source

Clinical validation of a real‐time machine learning‐based system for the detection of acute myeloid leukemia by flow cytometry

open access: yesCytometry Part B: Clinical Cytometry, EarlyView.
Abstract Machine‐learning (ML) models in flow cytometry have the potential to reduce error rates, increase reproducibility, and boost the efficiency of clinical labs. While numerous ML models for flow cytometry data have been proposed, few studies have described the clinical deployment of such models.
Lauren M. Zuromski   +10 more
wiley   +1 more source

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