Different Multimorbidity Measures Result in Varying Estimated Levels of Physical Quality of Life in Individuals with Multimorbidity: A Cross-Sectional Study in the General Population
Bibliographic record
Abstract
INTRODUCTION: Multimorbidity adversely affects health-related quality of life. Methodological factors may impact the magnitude of this relationship. OBJECTIVE: To evaluate how physical health-related quality of life varies in individuals with multimorbidity depending on the length of the list of candidate conditions considered. METHODS: Secondary analysis from PRECISE, a cohort study of the general adult population of Quebec, Canada. Multimorbidity was measured using the 21-chronic condition list from the Disease Burden Morbidity Assessment, and physical health-related quality of life was measured using the physical component summary (PCS) of SF-12v2. The PCS was calculated, (a) using 2 or more conditions from the 21-condition list (MM2+, 21) and then from a reduced 6-condition list (MM2+, 6) and (b) using three or more conditions from each list (MM3+, 21, and MM3+, 6). RESULTS: The analysis included 1,710 individuals (mean age 51.3, 40.5% men). Multimorbidity prevalence ranged from 63.8% (MM2+, 21 conditions) to 3.8% (MM3+, 6 conditions). The mean [95% CI] PCS dropped from 45.7 [CI: 45.0-46.3] (MM2+, 21) to 40.2 [CI: 38.7-41.8] (MM2+, 6) and from 44.2 [CI: 43.4-44.9] (MM3+, 21) to 34.8 [CI: 31.9-37.6] (MM3+, 6). CONCLUSION: The length of the list of candidate conditions considered has a great impact on the estimations of physical health-related quality of life.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".