Multimorbidity is common to family practice: is it commonly researched?
Bibliographic record
Abstract
OBJECTIVE: Family physicians often have to care for patients with several concurrent chronic conditions (multimorbidity or comorbidity). Consequently, they need to inform themselves by reading indexed publications on multimorbidity. This study aimed to assess how well the concept of multimorbidity was covered in the medical literature. Objectives were first, to quantify the literature on multimorbidity (or comorbidity) and to compare the number of publications on it with the number of publications on three common chronic conditions (asthma, hypertension, and diabetes), and second, to describe the articles on multimorbidity. DESIGN: Bibliometric study. METHOD: We consulted MEDLINE for the reference period 1990 to the end of 2002. The term "multimorbidity" and its various spellings was used as the search term. Comorbidity, asthma, hypertension, and diabetes were searched for using their respective MeSH terms. For comparison purposes, prevalence data were taken from published sources. Abstracts of articles relating to multimorbidity were reviewed and their content analyzed. MAIN OUTCOME MEASURES: Number and type of articles. RESULTS: Multimorbidity has a prevalence of 60% among people aged 55 to 74. This prevalence is much higher than that of asthma (6.5%), hypertension (29.6%), and diabetes (8.7%). Few articles in the medical literature deal specifically with multimorbidity (or comorbidity), however. For each article on multimorbidity, there are 74 on asthma, 94 on hypertension, and 38 on diabetes. Content analysis of abstracts of articles on multimorbidity revealed a high proportion of epidemiologic studies (50.0%) followed by validation studies (22.4%) and opinion pieces (11.8%). The few experimental studies on multimorbidity were not done in primary care settings. CONCLUSION: This study shows that the prevalence of multimorbidity is not matched by the number of indexed publications on it in the medical literature. To date, the number and diversity of articles on multimorbidity are both insufficient to provide scientific background for strong evidence-based care of patients affected by multiple concurrent chronic conditions. Research is needed to increase knowledge and understanding of this important clinical topic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.135 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.030 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".