ReviewJournal of general internal medicine2014
Addressing multimorbidity in evidence integration and synthesis.
Review in Journal of general internal medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Analytical approaches to evaluate risk factors of multimorbidity: a systematic scoping review protocol.BMJ open · 2025Article
- Use of machine learning in geriatric clinical care for chronic diseases: a systematic literature review.JAMIA open · 2020Review
- Clinical practice guidelines for older people with multimorbidity and life-limiting illness: what are the implications for deprescribing?Therapeutic advances in drug safety · 2018Article
- An Official American Thoracic Society Workshop Report. A Framework for Addressing Multimorbidity in Clinical Practice Guidelines for Pulmonary Disease, Critical Illness, and Sleep Disorders.Annals of the American Thoracic Society · 2016Article
- Evidence-based medicine and the hard problem of multimorbidity.Journal of general internal medicine · 2014Article
- A framework for crafting clinical practice guidelines that are relevant to the care and management of people with multimorbidity.Journal of general internal medicine · 2014Review
- Multimorbidity and evidence generation.Journal of general internal medicine · 2014Review
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
Abstract
To minimize bias, clinical practice guidelines (CPG) for managing patients with multiple conditions should be informed by well-planned syntheses of the totality of the relevant evidence by means of systematic reviews and meta-analyses. However, deficiencies along the entire evidentiary pathway hinder the development of evidence-based CPGs. Published reports of trials and observational studies often do not provide usable data on treatment effect heterogeneity, perhaps because their design, analysis and presentation is seldom geared towards informing on how multimorbidity modifies the effect of treatments. Systematic reviews and meta-analyses inherit all the limitations of their building blocks and introduce additional of their own, including selection biases at the level of the included studies, ecological biases, and analytical challenges. To generate recommendations to help negotiate some of the challenges in synthesizing the primary literature, so that the results of the evidence synthesis is applicable to the care of those with multiple conditions. Informal group process. We have built upon established general guidance, and provide additional recommendations specific to systematic reviews that could improve the CPGs for multimorbid patients. We suggest that following the additional recommendations is good practice, but acknowledge that not all proposed recommendations are of equal importance, validity and feasibility, and that further work is needed to test and refine the recommendations.
Indexed as
Identifiers
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.