EVALUATING ORAL AND MAXILLOFACIAL HEALTH CHALLENGES IN INTRAVENOUS DRUG USERS: A CROSS-SECTIONAL STUDY OF DRUG REPLACEMENT THERAPY PARTICIPANTS AND NON-PARTICIPANTS.
Bibliographic record
Abstract
INTRODUCTION: Intravenous drug use has a significant impact on oral and maxillofacial health, often resulting in complications like tooth loss and osteomyelitis. This study investigates the differences in oral health between drug users enrolled in replacement therapy and those not yet participating, with the goal of assessing the impact of structured treatment programs. AIM: to evaluate and compare the prevalence of oral and maxillofacial complications among drug users involved in replacement therapy and those newly registering for treatment. METHODS: This cross-sectional study was conducted in Tbilisi, Georgia, at the Center for Mental Health and Prevention of Addiction. Participants were divided into two groups: 135 individuals registering for replacement therapy for the first time and 115 participants who had been enrolled for over a year. Data collection involved questionnaires and intraoral examinations, focusing on inflammatory signs, functional impairments, and complications like retained roots and signs of toxic osteomyelitis. Statistical analysis was performed using IBM SPSS Statistics version 23. RESULTS: The findings revealed that individuals without prior replacement therapy had significantly more severe inflammatory symptoms, including draining fistulas (12.6% vs. 3.5%, χ²(2)=8.61, p=0.013), exposed bone (12.6% vs. 0.9%, χ²(1)=13.74, p=0.000), and visualized sequestra (8.9% vs. 1.7%, χ²(1)=6.01, p=0.014). Functional impairments, such as difficulties in mouth opening (31.1% vs. 12.2%, χ²(1)=12.81, p=0.000) and tooth loosening (51.1% vs. 35.7%, χ²(1)=6.02, p=0.014), were also more prevalent in this group. Retained dental roots were notably higher in the posterior lower jaw among untreated individuals (72.6% vs. 53.9%, χ²(1)=9.41, p=0.002). CONCLUSION: The findings suggest that drug replacement therapy plays a significant role in reducing severe oral and maxillofacial complications. Integrating dental care into addiction programs is essential for addressing untreated oral health issues and preventing severe outcomes such as osteomyelitis. Longitudinal studies are recommended to further evaluate the long-term benefits of replacement therapy and refine intervention strategies.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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".