ReviewJournal of dental research2023
Quantitative Assessment of Systematic Bias: A Guide for Researchers.
Review in Journal of dental research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluation of the evidence on acetaminophen use and neurodevelopmental disorders using the Navigation Guide methodology.Environmental health : a global access science source · 2025Pooled it
- Unilateral Chewing Frequency and Self-Reported Mastication Satisfaction: A Cross-Sectional Study.Journal of oral rehabilitation · 2026Article
- Cohort and Case-Control Studies: Strengths, Limitations and Methodological Considerations.Journal of periodontal research · 2026Review
- Multi-cohort evaluation of "Don't know" responders to self-report oral health questions: Implications for etiologic research.Journal of periodontology · 2026Observational
- Salivary Characteristics and Other Risk Factors Associated with the Severity of Chemical and Mechanical Tooth Wear in At-Risk Groups: A Cross-Sectional Study.Journal of clinical medicine · 2025Article
- Quantitative bias analysis for mismeasured variables in health research: a review of software tools.BMC medical research methodology · 2025Review
- A closer look at regional differences in euthanasia practices in the Netherlands.Research in health services & regions · 2025Article
- Association between chronic pain and mental health among nurses in China: a national cross-sectional study.BMC nursing · 2025Article
- Specifying a target trial protocol to estimate the effect of preconception treatment of periodontitis on time-to-pregnancy: A commentary and applied example.Community dentistry and oral epidemiology · 2025Article
- Validity of individual self-report oral health measures in assessing periodontitis for causal research applications.Journal of periodontology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Observational research provides valuable opportunities to advance oral health science but is limited by vulnerabilities to systematic bias, including unmeasured confounding, errors in variable measurement, or bias in the creation of study populations and/or analytic samples. The potential influence of systematic biases on observed results is often only briefly mentioned among the discussion of limitations of a given study, despite existing methods that support detailed assessments of their potential effects. Quantitative bias analysis is a set of methodological techniques that, when applied to observational data, can provide important context to aid in the interpretation and integration of observational research findings into the broader body of oral health research. Specifically, these methods were developed to provide quantitative estimates of the potential magnitude and direction of the influence of systematic biases on observed results. We aim to encourage and facilitate the broad adoption of quantitative bias analyses into observational oral health research. To this end, we provide an overview of quantitative bias analysis techniques, including a step-by-step implementation guide. We also provide a detailed appendix that guides readers through an applied example using real data obtained from a prospective observational cohort study of preconception periodontitis in relation to time to pregnancy. Quantitative bias analysis methods are available to all investigators. When appropriately applied to observational studies, findings from such studies can have a greater impact in the broader research context.
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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.