The Ariadne principles: how to handle multimorbidity in primary care consultations
Bibliographic record
Abstract
Multimorbidity is a health issue mostly dealt with in primary care practice. As a result of their generalist and patient-centered approach, long-lasting relationships with patients, and responsibility for continuity and coordination of care, family physicians are particularly well placed to manage patients with multimorbidity. However, conflicts arising from the application of multiple disease oriented guidelines and the burden of diseases and treatments often make consultations challenging. To provide orientation in decision making in multimorbidity during primary care consultations, we developed guiding principles and named them after the Greek mythological figure Ariadne. For this purpose, we convened a two-day expert workshop accompanied by an international symposium in October 2012 in Frankfurt, Germany. Against the background of the current state of knowledge presented and discussed at the symposium, 19 experts from North America, Europe, and Australia identified the key issues of concern in the management of multimorbidity in primary care in panel and small group sessions and agreed upon making use of formal and informal consensus methods. The proposed preliminary principles were refined during a multistage feedback process and discussed using a case example. The sharing of realistic treatment goals by physicians and patients is at the core of the Ariadne principles. These result from i) a thorough interaction assessment of the patient's conditions, treatments, constitution, and context; ii) the prioritization of health problems that take into account the patient's preferences - his or her most and least desired outcomes; and iii) individualized management realizes the best options of care in diagnostics, treatment, and prevention to achieve the goals. Goal attainment is followed-up in accordance with a re-assessment in planned visits. The occurrence of new or changed conditions, such as an increase in severity, or a changed context may trigger the (re-)start of the process. Further work is needed on the implementation of the formulated principles, but they were recognized and appreciated as important by family physicians and primary care researchers.Please see related article: http://www.biomedcentral.com/1741-7015/12/222.
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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 | 0.073 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".