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
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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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".