A bibliometric analysis of multimorbidity from 2005 to 2019
Bibliographic record
Abstract
CONTEXT: Multimorbidity is frequently seen in primary care. We aimed to identify and analyze publications on multimorbidity, including those that most influenced this field. METHOD: A bibliometric analysis of publications from 2005 to 2019 in the PubMed database containing "multimorbidity" or "multi-morbidity" identified with the tool iCite. We analyzed the number of publications, total citations, the article-level metric Relative Citation Ratio (RCR), type of study, and journals with the most cited articles. RESULTS: The number of publications using "multimorbidity" has continuously increased since 2005 (2005-2009: 138; 2010-2014: 823; 2015-2019: 3068). The median number of total citations per article was 3. The median RCR was 1.04. Articles with RCR at or above the 97th percentile (RCR = 7.43) were analyzed in detail (n = 104). In 34 publications of this subgroup (33%), the word multimorbidity was used but was not the subject of study. The remaining top 70 publications included 32 observational studies, 22 reviews, five guideline statements, three analysis papers, two randomized trials, three qualitative studies, two measurement development reports, and one conceptual framework development report. The publications were produced by authors from 32 countries. They were published in 37 different journals, ranging from one to four articles in the same journal. CONCLUSIONS: We found a continuous increase in the number of publications about multimorbidity since 2005. However, our study suggests that the numbers should be considered only a general trend because multimorbidity was not the main subject in 33% of publications in a subgroup of 104 analyzed in detail.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.025 | 0.063 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; both teacher heads agree on what is shown here.
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".