The effect of health related quality of life on reported use of health care resources in patients with osteoarthritis and rheumatoid arthritis: a longitudinal analysis.
Bibliographic record
Abstract
OBJECTIVE: In today's cost conscious environment, health services researchers are consistently trying to find ways to predict future health care resource utilization (HCRU) and its associated costs. We evaluated the impact of health related quality of life (HRQL) on future HCRU in patients with arthritis. METHODS: A total of 642 patients with rheumatoid arthritis (RA) and 395 patients with osteoarthritis (OA) completed at least 2 and as many as 6 consecutive surveys at 6 mo intervals. Information collected included demographics, HRQL questionnaires [Medical Outcome Study Short Form 36 (SF-36), Western Ontario McMaster Universities Osteoarthritis Index (WOMAC), and the Stanford Health Assessment Questionnaire (HAQ)], and HCRU over the previous 6 months. Longitudinal data analysis was perfomed to assess the effect of HRQL on future HCRU. RESULTS: Statistically significant associations between HCRU and HRQL variables were noted. Higher rates of HCRU were found in those in the worst quarter compared with those in the best quarter of HRQL. With the HAQ, OA and RA patients in the worst quarter reported a 199% (p < 0.05) and 48% (p < 0.05) increase in rheumatologist visits, respectively. With the WOMAC Function, increases were as high as 196% (p < 0.05) in rheumatologist visits for patients with OA. Patients with RA with a high level of HRQL as measured by the SF-36 (physical component score) reported a decrease of 31% (p < 0.01) in general practitioner visits and a decrease of 52% (p < 0.01) in hospitalization (mental component score). CONCLUSION: These findings suggest that HRQL may be used to predict future health care consumption. Such an approach may lead to a more efficient allocation of resources by providing useful information to health care providers and health care decision makers.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".