A systematic review of oral health status in substance abusers
Bibliographic record
Abstract
Background. Oral health is one of the most important issues for people and physicians, and it is very important to identify the factors that contribute to the damage to oral health. One of the factors that is always emphasized in harming oral health is drug addiction. Methods. This study is a systematic review and meta-analysis. We searched the electronic databases, including PubMed, Scopus, ProQuest, Google Scholar, and ISI for Persian and English articles and compared oral health indicators in patients with substance abuse with healthy subjects. The quality of the selected studies was measured by the Newcastle-Ottawa Scale. Heterogeneity of studies was performed using the Q test and I-square index. In case of heterogeneity of studies, a random effect model was used to combine the results. Publication bias was performed by funnel curves and Egger’s and Begg’s tests. Results. Substance abuse had a significant effect on (std dif in means 1/657[1.873-1/442], P<0/001) and it has a strong positive and significant effect on plaque index (OR. 1/42; 95% CI 1/18-1/7), P= 0/0002. Conclusion. The mean DMFT was higher in people with drug abuse than in healthy people. Periodontal problems are also more common in people with drug abuse than in healthy people. Hence, the oral health status of these people needs more attention. Practical Implications. Responsible organizations and social dentists should pay more attention to oral and dental health of substance abusers.
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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 | 0.012 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".