The Coexistence of Terms to Describe the Presence of Multiple Concurrent Diseases
Bibliographic record
Abstract
Background Consensus on terminology for multiple diseases is lacking. Because of the clinical relevance and social impact of multiple concurrent diseases, it is important that concepts are clear. Objective To highlight the diversity of terms in the literature referring to the presence of multiple concurrent diseases/conditions and make recommendations. Design A bibliometric analysis of English-language publications indexed in the MEDLINE database from 1970 to 2012 for the terms comorbidity, multimorbidity, polymorbidity, polypathology, pluripathology, multipathology, and multicondition, and a review of definitions of multimorbidity found in English-language publications indexed from 1970 to 2012 in the MEDLINE and SCOPUS databases. Results Comorbidity was used in 67,557 publications, multimorbidity in 434, and the other terms in three to 31 publications. At least 144 publications used the term comorbidity without referring to an index disease. Thirteen general definitions of multimorbidity were identified, but only two were frequently used (91% of publications). The most frequently used definition (48% of publications) was “ more than one or multiple chronic or long-term diseases/conditions”. Multimorbidity was not defined in 51% of the publications using the term. Conclusions Comorbidity was overwhelmingly used to describe any clinical entity coexisting with an index disease under study. Multimorbidity was the term most frequently used when no index disease was designated. Several definitions of multimorbidity were found. However, most authors using the term did not define it. The use of clearly defined terms in the literature is recommended until a general consensus on the terminology of multiple coexistent diseases is reached.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".