Printed in Great Britain Health Outcomes of Oral Disorders
Bibliographic record
Abstract
While there is a substantial body of data on patterns of dental disease in both adult and child populations, there is relatively little information concerning the consequences of oral disorders for well-being and the quality of life. In addition, dentistry lacks measures of health outcomes for use in dental health surveys and clinical trials. However, interest in this area is growing and a number of oral-condition specific health status measures have been developed over the last 10 years. The most sophisticated is the Oral Health Impact Profile (OHIP) which is being developed and tested by research teams in Australia, Canada and the US. This consists of 49 items organized into seven sub-scales which address the way in which oral conditions compromise functioning and social and psychological well-being. A study of older adults in Canada which used OHIP found that it had good measurement properties. Nevertheless, there was only a weak association between scores on this measure and clinical indicators of oral disease. Further analysis revealed that social factors were as important as clinical factors in explaining the health outcome of oral disorders. This finding is consistent with contemporary concepts of health and provides some evidence to support the view that the social context in which we live is important in shaping responses to disease and the experience of health and illness. In most industrialized countries repeated national and local dental health surveys of adult and child popu-lations have produced a substantial body of data on
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.235 | 0.061 |
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".