Key factors to consider when measuring multimorbidity
Bibliographic record
Abstract
BACKGROUND: There are multiple multimorbidity measures but little consensus on which measures are most appropriate for different circumstances. OBJECTIVE: To share insights gained from discussions with experts in the fields of ageing research and multimorbidity on key factors to consider when measuring multimorbidity. DESIGN: Descriptive study of expert opinions on multimorbidity measures, informed by literature to identify available measures followed by a face-to-face meeting and an online survey. RESULTS: The expert group included clinicians, researchers and policymakers in Canada with expertise in the fields of multimorbidity and ageing. Of the 30 experts invited, 15 (50%) attended the in-person meeting and 14 (47%) responded to the subsequent online survey. Experts agreed that there is no single multimorbidity measure that is suitable for all research studies. They cited a number of factors that need to be considered in selecting a measure for use in a research study including: (1) fit with the study purpose; (2) the conditions included in multimorbidity measures; (3) the role of episodic conditions or diseases; and (4) the role of social factors and other concepts missing in existing approaches. CONCLUSIONS: The suitability of existing multimorbidity measures for use in a specific research study depends on factors such as the purpose of the study, outcomes examined and preferences of the involved stakeholders. The results of this study suggest that there are areas that require further building out in both the conceptualization and measurement of multimorbidity for the benefit of future clinical, research and policy decisions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".