Validity of medication‐based co‐morbidity indices in the Australian elderly population
Bibliographic record
Abstract
OBJECTIVES: To determine the validity of two medication-based co-morbidity indices, the Medicines Disease Burden Index (MDBI) and Rx-Risk-V in the Australian elderly population. METHODS: In Phase I, the sensitivity and specificity of both indices were determined in 767 respondents from wave 6 of the Australian Longitudinal Study of Ageing (ALSA). Medication-defined index disease categories were compared to self-reported medical conditions. Correlation with self-rated health was examined and Cox proportional hazards models were used to assess the predictive validity for mortality. Phase II verified the predictive ability of Rx-Risk-V in a sample of 213,191 veterans from Australian Department of Veterans' Affairs (DVA) database. RESULTS: MDBI and Rx-Risk-V scores could be calculated for 28% and 73% of the ALSA sample respectively. Both indices had high specificities and low to moderate sensitivities compared to self-reported medical conditions. Total weighted scores were significantly related to self-rated health (p<0.001). Both indices were predictive of mortality (Hazard Ratio (HR) =3.690 (95% CI 2.264-6.015) for MDBI and HR 1.079 (95% CI 1.045-1.114) for Rx-Risk-V. The predictive validity for mortality of Rx-Risk-V was confirmed using DVA data (HR= 1.090, 95% CI 1.088-1.092). CONCLUSIONS: Medication-based co-morbidity indices Rx-Risk-V and MDBI are valid measures of co-morbidity. However, Rx-Risk-V detects more comorbidity in the Australian elderly population and is likely to be a more suitable index to use in administrative datasets, particularly where studies include large numbers of outpatients.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".