Standard of dental prescriptions for antibiotics dispensed in the systempublic oral health services in the state of Minas Gerais
Bibliographic record
Abstract
Antibiotics, along with analgesics and anti-inflammatory drugs, are the most commonly used medications in dentistry. The prescription of antibiotics by dental surgeons happens all over the world, and the irrational use of these drugs can result in therapeutic failure, increased risk of adverse reactions and economic impact, besides being the main cause of antimicrobial resistance. The literature points out that pain of dental origin is rarely caused by a bacterial infection requiring antibiotic medication and is usually best managed with the use of analgesics and local dental procedures. The results of surveys conducted in England and Canada suggest that antibiotic prescriptions by dental surgeons are increasing alarmingly. It is also known that the pattern of antibiotic prescribing can be influenced by both clinical and non clinical factors. In this sense, generating information on antibiotic consumption is essential for countries to adopt measures to raise awareness among the population and health professionals about the appropriate use of these drugs, monitor the impact of interventions, and improve the process of acquiring, prescribing, and dispensing these drugs. The aim of this study was to analyze the possible association between dental antibiotic prescriptions in the public sector of a southeastern Brazilian state, health services characteristics, and municipal social characteristics. The study design was of the ecological type, the year analyzed was 2017, and the data were obtained from the database of the Integrated Pharmaceutical Assistance Management System. The outcome variable of the first article of this PhD Thesis was the number of Defined Daily Doses (DDD) per 1,000 inhabitants/year of the municipalities. The outcome variable of the second article was the municipalities' adherence to a dental prescription information system. The database was analyzed initially in Excel version 2016 program (Microsoft, Seattle, USA) and later in SPSS version 25.0 program (IBM SPSS Statistics for Windows, Armonk, NY, USA). The CART (Classification And Regression Tree) technique was used to determine the influence of the social characteristics of the municipalities (Human Development Index, Gini Index, proportion of rural population, proportion of beneficiary families of the Bolsa Família Program, rural/urban typology, whether or not the municipality is the headquarters of a Dental Specialties Center, seat of a Health Macro-region and Microregion) and the characteristics of oral health services (oral health coverage in the Family Health Strategy and Primary Health Care, population coverage of first dental consultation, number of dentists and oral health teams per 1000 inhabitants, and percentage of individual preventive and restorative dental procedures). Antibiotics were the most prescribed drugs by dental surgeons in the public sector surveyed, with penicillins being the most prescribed group. The overall average of DDD/1000hab/year, for the 421 municipalities surveyed, was 96.54. It was concluded that socioeconomic factors and organization of health services were associated with the use of antibiotics. There is a need for advances in the surveillance of antibiotic prescribing in public oral health services in the state of Minas Gerais
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".