Associations between Multimorbidity Clusters and All-Cause Mortality, Quality of Life and Physical Function: A Systematic Review Protocol
Bibliographic record
Abstract
Background Multimorbidity, defined as the coexistence of two or more chronic health conditions in affected individuals, is recognised as a significant global public health concern. While previous research has identified common multimorbidity clusters, the relationships of these clusters with health outcomes is not well understood. Aim To systematically retrieve, synthesise, and appraise the available evidence on the association between multimorbidity clusters and mortality, quality of life (QoL), and physical function. Methods We will conduct a systematic search of PubMed, EMBASE, Web of Science, Ebsco APA PsyInfo and CINAHL from inception until April 2025. Eligible studies will include observational studies (e.g. cohort, cross-sectional or case control) investigating the association between multimorbidity clusters and all-cause mortality, QoL, or physical function in community- dwelling adult populations. Multimorbidity clustering will be defined as the non-random co-occurrence of chronic health conditions. Outcomes of interest include all-cause mortality, health-related quality of life (QoL) (i.e., self-perceived health status), and physical function (e.g., functional independence/physical performance)). Risk of bias will be evaluated using appropriate tools, e.g., the risk of bias in observational studies of exposures (ROBINS-E) tool. Findings will be synthesised narratively, and if feasible, a meta-analysis will be performed. Given the anticipated heterogeneity of multimorbidity clusters, a robust methodological framework, informed by existing multimorbidity literature and stakeholder engagement, will be applied to facilitate comparability across studies. Conclusion This review will lay out and summarise current evidence on the association between different multimorbidity clusters and key health outcomes, including all-cause mortality, QoL, and physical function. It will address methodological approaches used to investigate the multimorbidity-health outcomes associations of interest and summarise current evidence. The findings will enhance our understanding of the unique burden imposed by different clusters of chronic conditions and may inform improvements in healthcare policy, the delivery of health services and patient care. Prospero Registration Number: CRD420251014288 (Date of Registration: 25 Jun 2025 13:08 UTC version 1.0 published).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.084 | 0.094 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.017 | 0.015 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.088 | 0.011 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".