Elderly Population Improve Quality of Oral Life
Bibliographic record
Abstract
AbstractIntroduction: The Geriatric Oral Health Assessment Index (GOHAI) has proven to be anexcellent tool for detecting oral disorders. On the other hand, the relative responsiveness of thismeasure to detect clinically meaningful change is not entirely clear .The GOHAI is a 12-itemself-reported index, validated first in the United States in an elderly Caucasian sample andsubsequently in Hispanic, African American, Chinese, French and Spanish samples.Aim and objectives: To provide necessary data for oral health administrators to plancomprehensive programmes to improve quality of life in elderly population.Methodology: All elderly individuals in these old age homes formed the study population.WHO Oral health assessment, 1997 and GOHAI was used in the study.Results: About 29.3% (n=66) said that they were sometimes able to eat without feelingdiscomfort while 5.3% (n=12) were often able to eat without discomfort. About half of theparticipants i.e. 50.7%(n=114) said they never did while 2.7% (n=6) said they often or alwayshad limit their contacts with people. About 42.7% (n=96) said they never were, while 4% (n=9)said they seldom were pleased with the looks of their teeth, gums or dentures.Conclusion: The study focus on the need to conduct similar studies with more diversepopulation and influence the policy makers in the country to include geriatric oral health careneeds in National Oral Health Policy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".