Results 171 to 180 of about 69,426 (314)

Tubulointerstitial Nephritis Induced by Adalimumab

open access: yesTurkish Journal of Nephrology
Yasmine Salem Mahjoubi   +6 more
doaj   +1 more source

Comparative analysis of the characteristics and trends of adverse drug reaction reports from patients in Japan and the Japanese Adverse Drug Event Report database

open access: yesBritish Journal of Clinical Pharmacology, Volume 92, Issue 2, Page 515-524, February 2026.
Aims Spontaneous reporting of adverse drug reactions (ADR) after a product has reached the market is essential for drug safety. This study analysed patient ADR reports and compared them with reports from the Japanese Adverse Drug Event Report (JADER) database to identify differences and trends.
Masami Tsuchiya   +5 more
wiley   +1 more source

Optimizing Physician Surveys in Pharmacovigilance Using ecancer Online Community [PDF]

open access: bronze, 2017
J.J. Body   +7 more
openalex   +1 more source

Virtual healthcare compared to hospital care for acute and post‐acute illness in adults: A systematic review and meta‐analysis of randomized controlled trials

open access: yesBritish Journal of Clinical Pharmacology, Volume 92, Issue 2, Page 374-395, February 2026.
Aim To evaluate the clinical effectiveness, cost‐effectiveness, quality of life (QoL) and patient/caregiver satisfaction associated with VWs/HaH vs. traditional inpatient care in adults with acute or post‐acute illness. Methods We conducted a systematic review and meta‐analysis of randomized controlled trials (RCTs), following PRISMA 2020 guidelines ...
Rana A. Malhis   +6 more
wiley   +1 more source

Dupilumab in moderate‐to‐severe prurigo nodularis: Real‐world data from early access program

open access: yes
Journal of the European Academy of Dermatology and Venereology, EarlyView.
Marie Jachiet   +14 more
wiley   +1 more source

Machine learning methods for predicting adverse drug events: A systematic review

open access: yesBritish Journal of Clinical Pharmacology, Volume 92, Issue 2, Page 422-444, February 2026.
Abstract Predicting adverse drug events (ADEs) in outpatient settings is crucial for improving medication safety, identifying high‐risk patients and reducing health‐care costs. While traditional methods struggle with the complexity of health‐care data, machine learning (ML) models offer improved prediction capabilities; however, their effectiveness in ...
Niaz Chalabianloo   +8 more
wiley   +1 more source

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