Associations between self‐reported dental status and diet
Bibliographic record
Abstract
The purpose of this study was to develop a battery of dental, nutritional and psychological health survey measures and to use this survey instrument to explore links between age, tooth loss and dietary risk. The survey was undertaken in a dental school and hospital. Forty-nine consecutive patients (age range 25-74 years) participated in this pilot study and completed the health survey instrument. A quarter of the patients reported changing dietary habits due to dental problems, 56% reported difficulty in chewing as a result of problems with their teeth or dentures, and 36% reported having to interrupt meals due to dental difficulties. Tooth number was associated with MNA scores (0.35, P=0.03, Pearson's correlation coefficient) and reported number of foods eaten (0.33, P=0.04, Pearson's correlation coefficient) from the questionnaire checklist. Lower MNA scores were associated with age (F=6.54; d.f.=1, 46; P<0.01) indicating that older adults were more at risk of poor nutritional status. Overall health was not rated as an important factor influencing food choice, and only 14% of the sample felt that they had nutritional problems. Poor diet and impaired food choice was associated with declining numbers of teeth and increasing age. Older adults may require dietary advice to increase awareness of the importance of a healthy diet.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".