Distinction of quality of life, health related quality of life, and health status in children referred for rheumatologic care.
Bibliographic record
Abstract
OBJECTIVE: Current health status measures [sometimes called quality of life (QOL) measures] are based on the values of their designers. QOL, though, reflects the idiosyncratic values of each individual. We investigated whether children referred for rheumatologic care differentiate between the concepts of health related quality of life (HRQOL), overall QOL, and health status. METHODS: One hundred twenty-two consecutive children seen at a pediatric rheumatology referral clinic completed a new global self-report quality of life scale (Quality of My Life), a functional impairment scale (Childhood Health Assessment Questionnaire), and a disease severity visual analog scale. Sixty children were seen for a followup assessment. RESULTS: HRQOL was somewhat lower than overall QOL (median 6.6 vs 8.6 out of 10; respectively) in this sample of patients. Our subjects did differentiate between overall QOL and HRQOL and health status. Health status, as measured by disease severity, accounted for only a moderate amount of variability in HRQOL (R2 = 0.25, p< or =0.0001). Health status measured by functional disability accounted for even less of the variability in HRQOL (R2 = 0.047, p = 0.013). Similarly, HRQOL accounted for only a moderate amount of the variability seen in overall QOL (R2 = 0.31, p< or =0.0001). CONCLUSION: The goal of most health professionals is to improve their patients' overall QOL. QOL, though, appears to be a broad and idiosyncratic construct affected only moderately by health. Health status, global HRQOL, and overall QOL all provide independent information. All 3 measures should be considered for use in research studies. HRQOL and overall QOL reflect patients' own values, and therefore may offer important information for clinicians in addition to health status.
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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 | 0.002 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".