Multimorbidity's research challenges and priorities from a clinical perspective: The case of ‘Mr Curran’
Bibliographic record
Abstract
Older patients, suffering from numerous diseases and taking multiple medications are the rule rather than the exception in primary care. A manifold of medical conditions are often associated with poor outcomes, and their multiple medications raise additional risks of polypharmacy. Such patients account for most healthcare expenditures. Effective approaches are needed to manage such complex patients in primary care. This paper describes the results of a scoping exercise, including a two-day workshop with 17 professionals from six countries, experienced in general practice and primary care research as well as epidemiology, clinical pharmacology, gerontology and methodology. This was followed by a consensus process investigating the challenges and core questions for multimorbidity research in primary care from a clinical perspective and presents examples of the best research practice. Current approaches in measuring and clustering multimorbidity inform policy-makers and researchers, but research is needed to provide support in clinical decision making. Multimorbidity presents a complexity of conditions leading to individual patient's needs and demanding complex processes in clinical decision making. The identification of patterns presupposes the development of strategies on how to manage multimorbidity and polypharmacy. Interventions have to be complex and multifaceted, and their evaluation poses numerous methodological challenges in study design, outcome measurement and analysis. Overall, it can be seen that complexity is a main underlying theme. Moreover, flexible study designs, outcome parameters and evaluation strategies are needed to account for this complexity.
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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 | 0.039 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".