Refining the measurement of the economic burden of chronic diseases in Canada.
Bibliographic record
Abstract
This article presents an analysis of the economic burden of a number of chronic diseases in Canada. In the analysis, we adjusted our measure of utilization of physician and hospital services for co-existing chronic diseases, which we found to be widely prevalent and to have an impact on resource use. Using data from the 1999 National Population Health Survey, we developed resource use rankings for several chronic conditions and decomposed these measures into prevalence and per-person utilization components. Our results indicate that, for the diseases with the greatest impact, resource use measures are driven more by disease prevalence than intensity of resource use. The diseases with the highest overall degree of resource use are back pain, arthritis or rheumatism, high blood pressure and migraines for people under 60; and arthritis or rheumatism and high blood pressure for people over 60. Our methods can be used to forecast the overall relative impact of resource use due to disease prevalence and per-person resource use intensity for various conditions.
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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 | 0.006 | 0.001 |
| 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".