Dor dentária em usuários de Substâncias Psicoativas dos CAPS AD de Vitória, Vila Velha e Serra, ES, Brasil
Bibliographic record
Abstract
Toothache is a public health problem that causes great inconvenience to psychoactive substances users. The objective was to verify the prevalence of dental pain and its associations among psychoactive substances users from Alcohol and Drug Psychosocial Care Centers (CAPS AD) in Vitoria, Vila Velha and Serra, Espírito Santo, Brazil. A transversal study was conducted with 280 participants between June 2015 and February 2016, using five scripts: one for socio-demographic data and health perception; another for oral health; the Oral Health Impact Profile; the Alcohol Smoking and Substance Involvement Screening Test and the World Health Organization Quality of Life Test. Data were organized in frequency tables and analyzed with the SPSS 20 statistical package. Comparisons were made with Fisher's test and the Odds Ratio (OR) was used to check the strength of the association between the variables. The prevalence of pain in the population studied was 59.3%, and individuals whose quality of life was impacted due to their oral conditions were 2.2 times more likely to report toothache in the last 6 months. The population studied showed a high prevalence of dental pain and the study indicates that dental pain interferes in the quality of life of psychoactive substances users who are treated at CAPS AD services in these three cities.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".