Longitudinal construct validity of the minimum data set health status index
Bibliographic record
Abstract
BACKGROUND: The Minimum Data Set Health Status Index (MDS-HSI) is a generic, preference-based health-related quality of life (HRQOL) measure derived by mapping items from the Resident Assessment Instrument - Minimum Data Set (RAI-MDS) assessment onto the Health Utilities Index Mark 2 classification system. While the validity of the MDS-HSI has been examined in cross-sectional settings, the longitudinal validity has not been explored. The objective of this study was to investigate the longitudinal construct validity of the MDS-HSI in a home care population. METHODS: This study utilized a retrospective cohort of home care patients in the Hamilton-Niagara-Haldimand-Brant health region of Ontario, Canada with at least two RAI-MDS Home Care assessments between January 2010 and December 2014. Convergent validity was assessed by calculating Spearman rank correlations between the change in MDS-HSI and changes in six validated indices of health domains that can be calculated from the RAI-MDS assessment. Known-groups validity was investigated by fitting multivariable linear regression models to estimate the mean change in MDS-HSI associated with clinically important changes in the six health domain indices and 15 disease symptoms from the RAI-MDS Home Care assessment, controlling for age and sex. RESULTS: The cohort contained 25,182 patients with two RAI-MDS Home Care assessments. Spearman correlations between the MDS-HSI change and changes in the health domain indices were all statistically significant and in the hypothesized small to moderate range [0.1 < ρ < 0.5]. Clinically important changes in all of the health domain indices and 13 of the 15 disease symptoms were significantly associated with clinically important changes in the MDS-HSI. CONCLUSIONS: The findings of this study support the longitudinal construct validity of the MDS-HSI in home care populations. In addition to evaluating changes in HRQOL among home care patients in clinical research, economic evaluation, and health technology assessment, the MDS-HSI may be used in system-level applications using routinely collected population-level data.
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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 | 0.011 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".