Associations between chronic disease, age and physical and mental health status
Bibliographic record
Abstract
This paper examines the associations between chronic disease, age, and physical and mental health-related quality of life (HRQOL), using data collected in 10 studies representing five chronic conditions. HRQOL was measured using the SF-36 or the shorter subset, SF-12. Physical Component Summary (PCS) and Mental Component Summary (MCS) scores were graphed by condition in age increments of 10 years, and compared to age- and sex-adjusted normative data. Linear regression models for the PCS and MCS were controlled for available confounders. The sample size of 2418 participants included 129 with renal failure, 366 with osteoarthritis (OA), 487 with heart failure, 1160 with chronic wound (leg ulcer) and 276 with multiple sclerosis (MS). For the PCS, there were large differences between the normative data and the mean scores of those with chronic diseases, but small differences for the MCS. Female gender and comorbid conditions were associated with poorer HRQOL; increased age was associated with poorer PCS and better MCS. This study provided additional evidence that, while physical function could be severely and negatively affected by both chronic disease and advanced age, mental health remained relatively high and stable.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".