ReviewCureus2024
A Longitudinal Analysis of Black Box Warnings: Trends and Implications for Drug Safety.
Review in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- US Food and Drug Administration black box warnings: Characteristics of drug classes and adverse effects.The Journal of pharmacology and experimental therapeutics · 2026Article
- Silent Dangers in Elderly Pharmacotherapy: The Interplay of Polypharmacy, Multimorbidity, and Drug Interactions.Journal of evaluation in clinical practice · 2025Review
- Safety of up to 140 Daily Applications of Recombinant Human Platelet-Derived Growth Factor (rhPDGF-BB) Onto Skin Wounds: Unboxing the Evidence.Wound repair and regeneration : official publication of the Wound Healing Society [and] the European Tissue Repair SocietyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A black box warning, signaling potential life-threatening adverse effects of medications or medical devices, is crucial for public and healthcare professional awareness. Comprehending and adhering to these warnings can prevent serious harm. This review aims to elucidate their significance. Data on drugs with black box warnings were collected from the Food and Drug Administration's (FDA's) official website using the search term 'Boxed warnings' from January 1, 2015, to January 31, 2024. A Microsoft Excel spreadsheet (Microsoft Corporation, Redmond, WA, USA) containing black box warnings for this period was downloaded from the FDA's website. Additional parameters, such as drug class and whether the warnings were new or existing, were added to the downloaded spreadsheet. The collected data were organized by year, categorizing new and existing warnings, along with details on the evidence source, system-wise classification, and black box warnings for commonly used drugs, including their clinical significance. Results show that in the past decade, 40% of black box warnings were issued in 2023, followed by 12% in 2022. Most warnings (67%) comprised existing ones with minor revisions while 29% were new. Nine existing warnings were removed during the period. Post-marketing studies predominantly provided evidence for these warnings. Neuropsychiatric concerns like addiction potential (31%), suicidal tendency (7%), and hypersensitivity reactions (12%) were the frequently encountered black box warnings. Black box warnings play a crucial role in highlighting the serious adverse effects of medications. Neuropsychiatric warnings have been frequent over the past decade. Awareness of these warnings is essential to prevent adverse effects and enhance patient care, especially concerning drugs like guaifenesin/hydrocodone bitartrate, zolpidem, and montelukast commonly encountered in clinical practice.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.