Evaluation of Oral Therapeuthical and Surgical Treatment Needs among Retirement Age Population in Different Countries
Bibliographic record
Abstract
Evaluation of Oral Therapeuthical and Surgical Treatment Needs among Retirement Age Population in Different Countries Oral health in connection with quality of life is affected by such functional factors as dental decay and its complications, untreated tooth roots, oral mucosal diseases and inflammations, precancerous diseases, cancers, pain in temporomandibular joints, xerostomia and partially or fully edentulous jaws. It has been noted in literature that among retirement age population the number of remaining teeth has increased and the number of untreated decayed teeth in developed countries for the last 20 years has decreased. Despite this fact the need to improve measures of oral health remains actual in this age group due to increasing prevalence of diagnosed oral diseases and number of extracted teeth and roots. Oral health indicators among retirement age population living in nursing homes in such countries as Canada, USA, UK, Finland, Denmark, Germany, Turkey, Brazil, Australia and Lithuania differ from the same age group indicators among self-dependent old people able to take care of themself. Oral health indicators of nursing homes residents in many countries are significantly worse than oral health indicators of the corresponding age group population. The proposed evaluation data of oral hygiene, periodontal status, DMF-T index, quality of existing and needs of new prosthodontics as well as oral mucosal disorders among retirement age population provides an important insight into therapeutic and surgical treatment provision in different countries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".