Patient under anti-clotting therapy in dental office – management, procedures, and complications: a survey research
Bibliographic record
Abstract
Introduction: An increasing number of patients are taking oral anti-clotting agents.As a result, there is an important patient safety concern in relation to a potential risk of bleeding complications following oral surgery.Objectives: The aim of the study was to evaluate the contemporary management of patients undergoing anticlotting therapy treated by Polish dentists, and to assess the most commonly observed complications following tooth extraction in that group of patients.Material and methods: The survey study was designed to include dentists and medical doctors.During the analysis, several aspects of medical professionals were considered, including years in the profession, field of specialty, medical sector (private or public), and size of the town where treatment was provided.The results were analyzed statistically.Results: Most of the dentists treat patients undergoing anti-clotting therapy more than once a week.Such patients are admitted more frequently into public sector clinics in minor cities, and treated by dentists specialized in oral or maxillofacial surgery.Most of general dentists do not exactly provide recommendation of international normalized ratio (INR) test.Most frequently observed complication in patients taking anti-clotting therapy after tooth extraction was hematoma.Conclusions: Medical professionals should evaluate their knowledge in the scope of most recent recommendations regarding dental treatment in special groups of patients, including patients on anti-clotting therapy.On the other hand, patient should be effectively informed by physician and pharmacist about the way of action of these drugs as well as the importance of providing information about their intake to every medical professional, including dentist.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".