Bibliographic record
Abstract
There is the potential for substantial gains in dental epidemiology with increasing use of population-level data and data linkage. The advantage of population record linkage, from an epidemiological perspective, is that it is not biased and no-one is excluded. This has important implications for human rights because generally the people who are excluded from studies or do not participate are the most marginalized. There are a number of places in the world that have high-quality population record linkage in the area of health including Oxford, Aberdeen, Rochester, Manitoba and Scandinavia. In Western Australia, there is a unique set of databases on the entire population, which includes detailed information on all births, deaths and hospital admissions since 1980, these are linkable with many other data sets. We can now link birth and hospital admissions with population databases for characteristics such as intellectual disability and birth defects. It is even possible to link Commonwealth data with State data. It is now possible to link birth defects data and midwives data with data from the Commonwealth Pharmaceutical Benefits Scheme as a form of pharmacovigilance, to detect potential associations between medicine use in pregnancy and birth defects such as cleft lip and palate. Data linkage is increasingly available in Australia, with other states setting up systems similar to Western Australia and may offer greater insight into oral health.
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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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".