Alexandro W L Chu, Gordon H Guyatt, Jason W Busse, Romina Brignardello-Petersen, Xiajing Chu, Daniel G Rayner, Paul Oykhman, Vanessa Wong, Yetiani Roldan, Diane R Baker, Leonard B Bacharier, Lisa A Beck, Moshe Ben-Shoshan, Bradley Chipps, Flavia Hoyte, Elliot Israel, David M Lang, Sameer K Mathur, Sharmilee M Nyenhuis, Paul M O'Byrne, Eric T Oliver, John Oppenheimer, Tamara T Perry, Katherine Rivera-Spoljaric, Sarbjit S Saini, Javed Sheikh, Kaharu Sumino, Susan Waserman, Elissa M Abrams, Aikaterini Anagnostou, Jonathan A Bernstein, Anne K Ellis, David B K Golden, Caroline C Horner, Dennis K Ledford, Jay Lieberman, Giselle Mosnaim, Matthew A Rank, Marcus Shaker, Julie Wang, David A Khan, Derek K Chu
Randomized controlled trials (RCTs) underpin evidence-based medicine, but a growing proportion of trials contain implausible, inaccurate, or fabricated data. When such studies are incorporated into systematic reviews, they distort effects estimates, inflate evidence certainty, and can mislead the guideline recommendations that the reviews aim to inform. Within eight recent American Academy of Allergy, Asthma & Immunology (AAAAI) and American College of Allergy, Asthma and Immunology (ACAAI) Joint Task Force on Practice Parameters (JTFPP) systematic reviews, 17% of trials published between 2021-2024 were problematic, and in a network meta-analysis of antihistamines for chronic urticaria, 39% of recent trials were excluded due to implausible data, statistical anomalies, and discrepancies between protocols and the final publication. The proliferation of generative AI further facilitates the ability to produce superficially credible, but flawed reports. Standardized tools have been developed to offer systematic approaches to detecting problematic trials, but robust safeguards such as verifiable ethics approval, prospective protocol registration, and adherence to structure reporting guidelines remain essential. As part of efforts to ensure quality research, the JTFPP will continue to update our systematic reviews to remove problematic trials as they are identified. We believe that this approach will promote the development of trustworthy guideline recommendations.