Investigating Oral and Dental Health and Quality of Life: A Cross-sectional Study
Bibliographic record
Abstract
Background: Oral and dental problems can affect the quality of life (QOL) and important aspects of a person’s life by disrupting social presence and interpersonal relationships. This study evaluates the oral and dental health and QOL in patients referring to the dental diagnosis department. Materials and Methods: This study was conducted on patients referred to the Diagnosis Department of Dental School at Hormozgan University of Medical Sciences, Bandar Abbas, Iran. After receiving the code of ethics, the decayed, missing, and filled permanent teeth (DMFT) questionnaire was used to measure dental caries, and the shortened questionnaire from the World Health Organization (WHO) was used to measure the QOL-related to the oral health of the patients. Results: The participants were 74(50%) women and 74(50%) men; meanwhile, 104(70.3%) subjects were married and 100(67.6%) did not have a chronic disease and only 28 people (18.9%) were smokers. Marital status had a significant relationship with the DMFT index (P<0.05). A significant relationship existed between smoking and the two scores (DMFT index and WHO QOL brief version (WHOQOL-BREF) (P<0.05). The relationship between occupation and WHOQOL-BREF indices was not significant (P>0.05); however, the relationship was significant with the DMFT index (P<0.05). Conclusion: Smoking increased tooth decay and demonstrated a decrease in the QOL. It is suggested to take measures in people who use tobacco to prevent oral and dental problems and also improve the QOL.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".