International comparison of cost of\nillness
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
All Western countries spent every year a lot of money on health care. \nCost of illness (COI) studies describe how health care costs are related\nto epidemiological and demographic variables. This report compares\nCOI-studies for some European and OECD countries as the Netherlands,\nGermany, France, Canada and Australia. It is demonstrated that\nCOI-studies can help to explain international differences in health\nexpenditure. It is also shown that acute care costs for major disease\ngroups are more or less the same in the different countries. Comparisons\nof long term care expenditure were hampered by country specific\ndefinitions and provisions. This report argues that cost of illness\nstudies can be useful: 1) to identify cross-national differences in\nhealth expenditure; 2) to monitor the cost development between\ncountries; 3) to investigate the effect of health care reforms from the\nperspective of disease, age and gender. The availability of appropriate\ndata is a critical condition here. International standardization of\ndata, classifications and methods is important, as well as for\nexpenditure data as with regard to utilization data and the allocation of\ncosts to disease, age and gender. A common approach will result in\nbetter cost of illness figures that serve the national and international\ndebate on health and health expenditure with a deeper understanding of\nthe interrelationships between demand and supply of health\ncare.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it