The Relationship of 60 Disease Diagnoses and 15 Conditions to Preference-Based Health-Related Quality of Life in Ontario Hospital-Based Long-Term Care Residents
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Bibliographic record
Abstract
BACKGROUND: Population-based diagnosis- and condition-specific health-related quality of life (HRQoL) scores are required for decision-making and research purposes. These HRQoL scores do not exist for hospital-based long-term care (LTC) residents. OBJECTIVE: To estimate the impact of 60 diseases and 15 conditions on caregiver-assessed preference-based HRQoL. METHODS: Residents in hospital-based LTC facilities in Ontario, Canada were identified from administrative databases containing resident minimum data set (MDS) assessments completed between August 1st, 2003 and March 31st, 2008. A preference-based HRQoL measure, the MDS Health-Status Index (MDS-HSI) score, was calculated for 66,193 residents. Average MDS-HSI scores and multivariate linear regression models were used to estimate the impact of the diagnoses and conditions, respectively. RESULTS: After adjusting for age, sex, and other diagnoses, aphasia exhibited the largest negative relationship to the MDS-HSI (-0.085), followed by cancer (-0.072) and Alzheimer disease (-0.062). Cancer was also the second most prevalent diagnosis (27.6%). Lack of balance was a common condition (87.3%) and it had the greatest negative relationship to MDS-HSI scores among the 15 conditions (-0.099). The diagnoses and conditions regression models had R values of 0.12 and 0.34, respectively, suggesting that clinical conditions provided better explanatory variables for the MDS-HSI than diagnoses. CONCLUSIONS: The findings suggest that diseases affect preference-based HRQoL differently in a hospital-based LTC population compared with previous studies in the general population. The population-based MDS-HSI scores from this study can be used as reference values in cost-effectiveness analyses for hospital-based LTC populations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it