ArticleDer Radiologe2019
[Risk of bias in controlled clinical trials].
Article in Der Radiologe, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Exergames in Older Adult Community Centers and Nursing Homes to Improve Balance and Minimize the Risk of Falls in Older Adults: A Systematic Review and Meta-Analysis.Healthcare (Basel, Switzerland) · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Results from clinical studies are often subject to the risk of bias (deviation from the truth, systematic error). Therefore, a critical appraisal of studies provides a useful strategy in evidence-based healthcare to safeguard against wrong decisions and resulting in overtreatment or undertreatment. This article explains the frequently encountered types of bias, differentiates between them and provides strategies for avoidance of systematic errors. In addition, the two established Cochrane tools with which the risk of bias can be assessed in randomized and non-randomized studies are presented. To highlight the most important components of these tools for bias assessment, examples of randomization, confounding, blinding, completeness of data and selective reporting are provided. Finally, it is shown that bias should not be confused with other study limitations, such as external validity and imprecision.
Indexed as
Identifiers
31346651What 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.