Towards Increased Visibility of Multimorbidity Research
Bibliographic record
Abstract
The number of people living with comorbidity, multimorbidity, or multiple chronic conditions, hereafter referred to as “multimorbidity” (see Box 1) [1,2], has become the norm rather than the exception in healthcare. In developed countries, approximately one in four adults have at least two chronic conditions [3,4], and over half of older adults have three or more [5]. Although the prevalence of multimorbidity increases with age, many studies have reported high rates of multimorbidity even among younger adults [6].\n\nMultimorbidity negatively impacts patient outcomes, including physical and psychological functioning, quality of life, and life expectancy [7,8]. It also complicates treatment and increases healthcare utilization and costs [9–11]. Despite representing a large – and growing – proportion of adults seen in primary care today, there is a major gap in our understanding of how best to address, meet, and satisfy the complex care needs of patients with multimorbidity [11]. The traditional single-disease model of care does not work for them, and multimorbidity should definitively not be considered as the simple juxtaposition of independent conditions [12,13].\n\nFortunately though, interest in multimorbidity is growing worldwide, and has become a healthcare and research priority [14,15]. An international community interested in multimorbidity research has recently emerged and become organized through different activities, such as the creation of the Journal of Comorbidity, a weblog that hosts and supports the exchanges from the International Research Community on Multimorbidity [16], the organization of an international forum [17] at the North American Primary Care Research Group (NAPCRG) congress, and the publication of an “ABC of Multimorbidity” [1]. \n\nJournal of Comorbidity 2016;6(2):42–45\n\n
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".