Multi-drug therapy in chronic condition multimorbidity: a systematic review
Bibliographic record
Abstract
BACKGROUND: Older populations often suffer from multimorbidity and guidelines for each condition are often associated with recommended drug therapy management. Yet, how different and specific multimorbidity is associated with number and type of multi-drug therapies in general populations is unknown. AIM: The aim of this systematic review was to synthesize the current evidence on patterns of multi-drug prescribing in family practice. METHODS: A systematic review on six common chronic conditions: diabetes mellitus, cardiovascular disease, cerebrovascular disease, chronic obstructive pulmonary disease (COPD), osteoarthritis and depression was conducted, with a focus on studies which looked at any potential combination of two or more multimorbidity. Studies were identified from searches of MEDLINE, EMBASE, PsychINFO, the Allied and Complementary Medicine Database (AMED) and the Health Management Information Consortium (HMIC) databases from 1960 to 2013. RESULTS: A total of eleven articles were selected based on study criteria. Our review identified very few specific studies which had explicitly investigated the association between multimorbidity and multi-drug therapy. Relevant chronic conditions literature showed nine observational studies and two reviews of comorbid depression drug treatment. Most (seven) of the articles had focused on the chronic condition and comorbid depression and whether antidepressant management had been optimal or not, while four studies focused on other multimorbidities mainly heart failure, COPD and diabetes. CONCLUSIONS: Very few studies have investigated associations between specific multimorbidity and multi-drug therapy, and most currently focus on chronic disease comorbid depression outcomes. Further research needs to identify this area as key priority for older populations who are prescribed high levels of multiple drug therapy.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".