Antibiotic prescription trends among dentists for oral infections with respect to their clinical experience and designation
Bibliographic record
Abstract
Background: Antibiotics are frequently used by dentists to manage oral infections. This has led to the development of antibiotic resistance. It is important to address this issue by raising awareness in dentists for a judicious use of antibiotics. The aim of this study is to assess the antibiotic prescription trends among dentists for oral infections with respect to their clinical experience and designation. Material and Methods: A Cross sectional descriptive study was conducted on 100 dentists working in College of Dentistry, Sharif Medical and Dental College, Lahore from January 2023 to January 2024. Dentists working in clinical sciences or those working in clinical settings irrespective of their age and gender were included in the study. Non-practicing dentists, those working in the basic dental sciences and those with a clinical experience of less than 6 months were excluded from the study. Data was collected by means of a pre-validated questionnaire. Statistical package for social sciences 23 was used for statistical analysis. Results: A statistically significant association between antibiotics prescription trends among dentists in fever due to oral infections (p=0.01), localized oral swelling (p=0.02), diffuse oral swelling (p=0.05), acute pulpitis (p=0.03), pericoronitis (p< 0.001), periodontal abscess (p< 0.001) and cellulitis (p< 0.001) and their designation was seen. The association between antibiotic prescription trends for pericoronitis (p=0.05) was significant with clinical experience of dentists with prevalence being higher in dentists with a clinical experience of 6-12 months. Conclusion: Majority of the house officers and dentists with lesser clinical experience were seen to prescribe more antibiotics in various oral infections This can be attributed to limited clinical experience which leads them to resort to antibiotics for resolution of oral infection more often than general dentists. Keywords: Antibiotic prescription, Dental practitioners, Clinical experience, Oral infections
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".