New Analytical Tools for Evaluating Dental Care Systems - Results for Germany and Selected Highly Developed Countries.
Bibliographic record
Abstract
OBJECTIVE: To propose new analytical tools that facilitate the obtention of quantifiable results for evaluating different dental care systems. METHODS: The paper describes the construction of a composite indicator that measures dental health on a population basis in one overall indicator, the Dental Health Index (DHI). If the DHI is combined with a Dental Care Cost Index, an efficiency index (EI) can be created. RESULTS: The use of these new instruments for analysing different dental care systems reveals that the Swedish and Danish populations enjoy the best dental health status, followed by US, Japanese, Australian and Canadian citizens. Germany ranks in the middle, while the Dutch and Finnish populations enjoy a lesser degree of dental health. Advanced dental health can be achieved in any oral healthcare system, irrespective of the underlying cost-sharing and funding structures. As a benchmark for industrialised countries, cost levels for dental care between 0.5% and 0.7% of GDP, seem to be the international norm. A population's dental status is determined by the degree to which preventive and tooth-preserving treatment approaches are practised, also amongst adults. CONCLUSION: The new instruments broaden the diagnostic possibilities for investigating different dental care systems. The greater the degree to which preventive and tooth-preserving treatment methods for the entire population are incorporated in daily clinical practice, the faster and better such systems progress and perform in terms of efficacy and efficiency.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".