Evaluation of oral health among people with multimorbidity in the marginalized population of Karachi, Pakistan: A multicenter cross-sectional study
Bibliographic record
Abstract
Background: Oral health is linked to physical and mental well-being. Oral disease is common among poor and socioeconomically disadvantaged people in developing and industrialized countries Objectives: This study assessed the oral health disease burden among people with multimorbidity in marginalized populations. Methods: This cross-sectional study was conducted across 16 locations in the slums of Karachi, Pakistan, to assess oral health disease problems among adults aged 18 to 70 with comorbidity or multimorbidity. The questionnaire covered the socioethnic, demographic, and disease status of people with oral health status. Data analyses were performed using SAS version 9.4. Results: Of the 16 designated slum locations, 870 individuals were considered for oral health screening. Gingivitis was highly prevalent, 29% among slum dwellers with multimorbidity of diabetes, hepatitis, and hypertension. Dandasa was widely used as a tooth-cleansing agent in 35% of the study population. By contrast, 45.4% of people showed unsatisfactory oral hygiene conditions. Pathan ethnicity showed the highest prevalence (i.e., 29.8% of dental problems with disease multimorbidity in 26.8% of Baldia Town residents of Karachi). Of the 870 individuals, the highest frequency of dental problems was found in the age group of 18-38 years (28-42.9%) and among female participants (53.8%). Conclusion: There is an urgent need for the global enhancement of public health programs, specifically focusing on implementing effective strategies to prevent oral illnesses, promote oral health, and address other chronic diseases in basic healthcare settings. Enhancing oral health poses significant difficulties, especially in less developed nations.
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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 | 0.001 | 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) | 0.001 | 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".