Understanding changes in complex care needs over time: key research insights into multimorbidity trajectories
Bibliographic record
Abstract
Multimorbidity, the coexistence of multiple chronic diseases or conditions, poses a major challenge for health-care systems worldwide. Traditional research has largely relied on cross-sectional studies, offering limited insight into multimorbidity evolution over time. This Personal View advocates for a paradigm shift towards longitudinal approaches that capture multimorbidity trajectories. Tracking the sequence, pace, and severity of disease accumulation can enhance our understanding of underlying mechanisms, inform early interventions, and improve patient care. Drawing on expert discussions from an international workshop held in Bielefeld, Germany, in May, 2024, we outline key themes and findings to guide future research on the dynamic processes underlying multimorbidity trajectories. Specifically, we summarise previous work, examine the challenges and opportunities of existing data resources, and highlight priority areas for further investigation. Advancing this field will require the standardisation of longitudinal multimorbidity phenotypes, integration of health and social care processes, and testing the usefulness of trajectories for patient-relevant outcomes and risk stratification. Progress will also depend on methodological innovation, patient and public involvement, harmonisation of diverse data sources, and close interdisciplinary collaboration. Ultimately, a trajectory-based framework for multimorbidity research can enable more personalised, efficient, and equitable health-care strategies, improving outcomes in ageing populations.
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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 | 0.021 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.021 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".