Oral Health Status of Geriatric Population: Cross Sectional Study
Bibliographic record
Abstract
Introduction: As the GOHAI appeared to have acceptable reliability and validity in all ages, it was recommended that the name of Geriatric Oral Health Assessment Index (GOHAI) be changed to the General Oral Health Assessment Index (GOHAI). Aim and objectives: Oral health related quality of life using GOHAI index. Methodology: Visit old age homes were present in the Jaipur city. The data was entered on to a personal computer and the analysis was done using the SPSS (statistical presentation software system) for windows (version 17). Descriptive statistics was carried out. The statistical significance was fixed at 0.05. Results: About 34.7% (n=78) never had any trouble biting or chewing any kind of food. Half of the participants (50.7%) were always able to swallow comfortably. Teeth or dentures of 66.7% (n=150) participants never prevented them while those of 1.3% (n=3) often prevented them from speaking the way they wanted. About 29.3% (n=66) said that they were sometimes able to eat without feeling discomfort while 5.3% (n=12) were often able to eat without discomfort. Conclusion: The study focus on the need to conduct similar studies with more diverse population and influence the policy makers in the country to include geriatric oral health care.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".