A person-centred clinical approach to the multimorbid patient with COPD
Bibliographic record
Abstract
Most patients with a chronic disease are multimorbid. This is particularly important in patients with chronic obstructive pulmonary disease (COPD), who on average have five other identified comorbidities that independently impact their health and increase their mortality risk. Using a modified Delphi method, we selected the 20 most important diseases associated with COPD and clustered them into five domains: mental, respiratory, cardiovascular, metabolic and multiple organs loss of tissue. We then developed a systematic approach to characterise the impact and clinical presentation of individual diseases within each cluster, and to define the priority and timing of measurement of the potential markers of disease presence and severity. Given the absence of integrated guidelines to treat multimorbid patients, we reviewed and selected individual disease guidelines or recommendations that can be accessed for specific information related to the management of each disease. In addition, we built a multimorbidity 'Health Dashboard' that, completed by the patient or health practitioner, can help identify the presence and severity of comorbid diseases. By using a practical comprehensive approach, it is possible to identify and characterise important comorbid diseases in patients with COPD, and to implement management tools that should help improve their outcome. This expert consensus commentary summarises patient-centred recommendations to manage comorbidities in COPD patients, aiming to improve quality-of-life and reduce disease burden through a holistic approach. Prospective pragmatic trials comparing such an approach with usual care for multimorbid patients with COPD including long-term follow-up are urgently needed.
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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 | 0.055 | 0.177 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.013 | 0.019 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".