Comorbidity versus multimorbidity: Why it matters
Bibliographic record
Abstract
The 'Journal of Comorbidity' is changing its name to the 'Journal of Multimorbidity and Comorbidity'.This may seem redundant, as many see 'comorbidity' and 'multimorbidity' as interchangeable terms.We believe it is important to highlight the distinction given the differences in how healthcare systems view patients with multiple chronic conditions (MCCs), and the important differences that arise in research and intervention development for these patients.In 1970, Feinstein first coined the term 'comorbidity' to describe 'Any distinct additional entity that has existed or may occur during the clinical course of a patient who has the index disease under study'. 1 From 1976, 2,3 the term 'multimorbidity' was increasingly used by health researchers to describe patients with MCCs.Due to the growing ambiguity around the use of the terms comorbidity and multimorbidity, in 1996 van den Akker et al. suggested clear definitions for both terms.4 They suggested that comorbidity be defined according to Feinstein's original definition and multimorbidity be defined as 'the cooccurrence of multiple chronic or acute diseases and medical conditions within one person'.4 In 2010, Boyd and Fortin provided a simpler definition of multimorbidity: 'the co-existence of two or more chronic conditions, where one is not necessarily more central than the others'.5
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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 | 0.023 | 0.094 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.031 | 0.034 |
| Insufficient payload (model declined to judge) | 0.025 | 0.012 |
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".