Prevalence of Multimorbidity Among Adults Seen in Family Practice
Bibliographic record
Abstract
PURPOSE There are few valid data that describe the extent of multimorbidity in primary care patients. The purpose of this study was to estimate its prevalence in family practice patients by counting the number of chronic medical conditions and using a measure that considers the severity of these conditions, the Cumula-tive Illness Rating Scale (CIRS). METHODS The study was carried out in the Saguenay region (Québec, Canada) in 2003. The participation of adult patients from 21 family physicians was solicited during consecutive consultation periods. A research nurse reviewed medical records and extracted the data regarding chronic illnesses. For each chronic condition, a severity rating was determined in accordance with the CIRS scoring guidelines. RESULTS The sample consisted of 320 men and 660 women. Overall, 9 of 10 patients had more than 1 chronic condition. The prevalence of having 2 or more medical conditions in the 18- to 44-year, 45- to 64-year, and 65-year and older age-groups was, respectively, 68%, 95%, and 99 % among women and 72%, 89%, and 97 % among men. The mean number of conditions and mean CIRS score also increased signifi cantly with age. CONCLUSIONS Whether measured by simply counting the number of conditions or using the CIRS, the prevalence of multimorbidity is quite high and increases signifi cantly with age in both men and women. Patients with multimorbidity seen in family practice represent the rule rather than the exception.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".