Unsubstantiated health claims for COVID-19 infections are led by cannabidiol: return of snake oil medicine
Bibliographic record
Abstract
BACKGROUND: The United States Food and Drug Administration (FDA) monitors, inspects, and enforces the promotion of products by companies that claim to mitigate, prevent, treat, diagnose, or cure COVID-19. The introduction of COVID-19-related diagnostics and therapeutics during the pandemic has highlighted the significance of rigorous clinical trials to ensure safety and efficacy of such interventions. The objective of this report is to provide a descriptive review of promotional violations of health products for COVID-19 infection. METHODS: Warning letters issued by the FDA's Center for Drug Evaluation and Research were retrieved over an 18 month period (March 6, 2020, to August 30, 2021) to identify promotional violations. FDA violation letters categorized as "Unapproved and Misbranded Products Related to Coronavirus Disease 2019 (COVID-19)" were reviewed. A content analysis was performed for each letter to identify categories for product type, promotional venue, violation type, and country of origin. For cannabidiol-related violations, a content analysis was repeated within its own product category. RESULTS: A total of 130 letters were reported. Across all letters, cannabidiol products were the most frequent subject of violation (15/130; 11.5%). Of the cannabidiol letters, all reported the promotion of unapproved products (15/15; 100%), misbranding (15/15; 100%), and/or had claims that lacked scientific substantiation (14/15; 93.3%). All promotional violations were linked to websites (15/15; 100%), along with other mainstream venues: Facebook, Instagram, YouTube, Twitter, LinkedIn, and email. Lastly, the cannabidiol products were described to provide therapeutic benefit to COVID-19, by acting as an anti-viral (5; 33.3%), pro-inflammatory (1; 6.7%), anti-inflammatory (7; 46.7%), immune-booster (5; 40%), immune-suppressor (2; 13.3%), and/or other (2; 13.3%). CONCLUSION: Despite the urgent need for COVID-19 treatments, promotional material by companies must comply with standard regulatory requirements, namely substantiation of claims. As the pandemic persists, the FDA must continue their efforts to monitor, inspect, and enforce violative companies. Cannabidiol-related substances led the spectrum of products with unsubstantiated claims to treat COVID-19 infection. Improving awareness among the public, healthcare providers, and stakeholders highlights the value of drug approval process, while protecting public safety.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".