ArticleBMJ medicine2025
Detection bias and the role of negative control outcomes.
Article in BMJ medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Pelargonium sidoides extract (EPs® 7630) versus usual care for acute bronchitis in Swiss primary care (PHYTOBRONCH): a pragmatic, open-label, randomised controlled trial.NPJ primary care respiratory medicine · 2026Trial
- Nonsteroidal Anti-Inflammatory Drugs and the Risk of Periodontitis Among Adults With Osteoarthritis: A Target Trial Emulation.Journal of clinical periodontology · 2026Article
- Exposure definition sensitivity unmasks hidden confounding in crystalloid target trial emulation.iScience · 2026Article
- How Negative Controls Are Used in Pharmacoepidemiology: A Methodological Scoping Review of Real-World Observational Studies of Glucagon-Like Peptide-1 Receptor Agonists.Pharmacoepidemiology and drug safety · 2026Article
- Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Investigators, patients, or clinicians knowing which treatment is assigned in pragmatic randomised trials and observational analyses can lead to detection bias (ie, systematic differences in determining outcomes between groups). A structural definition of detection bias with directed acyclic graphs is provided, together with several published examples. Why negative control outcomes are best placed to assess detection bias is discussed, and how to correctly select a negative control outcome for this purpose is explained.
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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 Socratic graph.