James Mackenzie Lecture 2011: multimorbidity, goal-oriented care, and equity
Bibliographic record
Abstract
![][1]</img> Today we face an important demographic and epidemiological transition, confronting us with the challenge of non-communicable diseases (NCDs), which occur more and more in the context of multimorbidity. In the next decade, multimorbidity will become the rule, no longer the exception: 50% of the those aged ≥65 years have at least three chronic conditions, whereas 20% of the ≥65-year group have at least five chronic conditions.1 In the case of COPD, for example, more than half of the patients have at least one comorbid disease.2 In recent years, not only Western countries, but also developing countries started with ‘chronic disease management-programmes’ to improve care. The design of those programmes include most frequently: strategies for case-finding, protocols describing what should be done and by whom, the importance of information and empowerment of the patient, and the definition of process- and outcome-indicators that may contribute to the monitoring of care. Wagner has described the different components of the Chronic Care Model (CCM) as developed in the context of primary health care.3 The CCM has inspired policy makers and providers all over the world and is widely accepted in the US and Canada, Europe, and Australia. Taking into account the epidemiological transition, we are faced with the question: ‘How will this approach work in a situation of multimorbidity’? Let us illustrate this with a patient from our general practice, we call her ‘Jennifer’ (Box 1). #### Box 1. Jennifer Jennifer is 75 years old. Fifteen years ago she lost her husband. She has been a patient at the practice for 15 years now. During these 15 years she has been through a difficult medical history: hip replacement surgery for osteoarthritis, hypertension, type 2 diabetes, and COPD. She lives independently at home, with some help from her youngest daughter, Elisabeth. I visit her regularly … [1]: /embed/graphic-1.gif
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.016 |
| Insufficient payload (model declined to judge) | 0.033 | 0.009 |
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".