ArticleBMJ open2020
Strategies to reduce antibiotic use in women with uncomplicated urinary tract infection in primary care: protocol of a systematic review and meta-analysis including individual patient data.
Article in BMJ open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed, 6 citations in OpenAlex.
- Non-Canonical Aspects of Antibiotics and Antibiotic Resistance.Antibiotics (Basel, Switzerland) · 2024Review
- Antibiotic utilization and symptom improvement in a retrospective cohort of women with urinary tract infection symptoms.International urogynecology journal · 2024Article
- A novel approach to screening and managing the urinary tract infections suspected sample in the general human population.Frontiers in cellular and infection microbiology · 2022Article
- Article
Corrections and comments
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Authors and funding
19 authors at 10 institutions in 9 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionUncomplicated urinary tract infection (UTI) in women is a common reason to present in general practice and is usually treated with antibiotics to reduce symptom severity and duration. Results of recent clinical trials indicate that non-antibiotic treatment approaches can also be effective. However, it remains unclear which patients would benefit from antibiotic treatment and which can effectively and safely be treated without antibiotics. This systematic review and meta-analysis aims to estimate the effect of treatment strategies to reduce antibiotic use in comparison with immediate antibiotic treatment and to identify prognostic factors and moderators of treatment effects. A further aim is to identify subgroups of patients benefiting from a specific therapy. METHODS AND ANALYSIS: A systematic literature search will be performed to identify randomised controlled trials which investigated the effect of treatment strategies to reduce antibiotic use in female adults with uncomplicated UTI compared with immediate antibiotic treatment. Therefore, the primary outcome of the meta-analysis is incomplete recovery. Anonymised individual patient data (IPD) will be collected. Aggregate data will be used for pairwise comparisons of treatment strategies using meta-analysis models with random effects accounting for potential between-study heterogeneity. Potential effect moderators will be explored in meta-regressions. For IPD, generalised linear mixed models will be used, which may be adjusted for baseline characteristics. Interactions of baseline variables with treatment effects will be explored. These models will be used to assess direct comparisons of treatment, but might be extended to networks. ETHICS AND DISSEMINATION: The local institutional review and ethics board judged the project a secondary analysis of existing anonymous data which meet the criteria for waiver of ethics review. Dissemination of the results will be via published scientific papers and presentations. Key messages will be promoted for example, via social media or press releases. PROSPERO REGISTRATION NUMBER: CRD42019125804.
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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 OpenQuestion graph.