Evaluating Oral Health-Related Quality of Life (OHRQoL) and Oral Health Needs of Geriatric Patients
Bibliographic record
Abstract
Objective: Aging has a profound impact on oral health, contributing to diminished functional capacity and reduced quality of life. Oral health-related quality of life (OHRQoL) is increasingly recognized as a public health concern, yet limited data exist for the geriatric population in Pakistan. To evaluate oral health needs and OHRQoL in the geriatric population in Lahore, Pakistan. Methods: A cross-sectional study was conducted at the Dental Teaching Hospital, University of Lahore, from September 2023 to January 2024 using non-probability sampling. Clinical oral health status was assessed using the decayed, missing, and filled teeth (DMFT) index and the Basic Periodontal Examination (BPE). OHRQoL was measured using the General Oral Health Assessment Index (GOHAI), and oral health needs were evaluated using the Oral Health Assessment Tool (OHAT) to complement clinical findings with a comprehensive assessment of oral conditions. Data were analyzed using SPSS version 25. Statistical tests included Pearson correlation, independent-sample t-tests, and one-way ANOVA with Bonferroni post hoc correction. Effect sizes and 95% confidence intervals were reported. Results: A total of 170 geriatric patients were included. The mean DMFT score was 10.29 ± 5.86, with a mean of 6.14 ± 5.17 missing teeth, and the mean BPE score was 2.41 ± 0.61. Poor oral cleanliness was significantly associated with higher BPE scores (p = 0.003, r = 0.32), and missing teeth were associated with increased dental pain (p = 0.004, r = 0.28). Lower education correlated with lower GOHAI scores (p = 0.003, η² = 0.07). The mean GOHAI score was 38.14 ± 3.53, indicating moderate OHRQoL. Conclusion: Geriatric patients demonstrated high levels of dental caries, tooth loss, and periodontal disease, all of which contributed to reduced OHRQoL. Strengthening preventive care, oral hygiene education, restorative services, and gerodontology training is essential to improve oral health outcomes and enhance the quality of life of older adults, particularly those from underserved groups.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".