Managing Cardiac Patients: Dentists’ Knowledge, Perceptions, and Practices
Bibliographic record
Abstract
OBJECTIVES: Dental patients may require invasive treatment, and awareness of their medical conditions is essential for optimal care. We assessed the knowledge, perceptions, and attitudes of dentists practicing in Saudi Arabia (SA) and their associations with managing patients with common cardiac conditions. METHODS: A national survey of knowledge and attitudes of practicing dentists towards patients with common cardiac conditions was conducted from May 2019 to July 2020 in SA. The survey comprised a newly developed, validated, electronic, self-administered English questionnaire. RESULTS: Overall, 282 dentists completed the survey, of whom 45.5% perceived cardiac patients as difficult to manage, while 64.5% stated that they refer these patients to cardiologists before dental intervention. Regarding knowledge about cardiac conditions, 72% achieved an overall knowledge score <55%; however, their infective endocarditis scores were better. Consultants and specialists (P < .001), those with a PhD/board certification (P = .013), dentists with prior education on cardiac patient management (P = .002), and those working with a cardiologist (P = .016) scored higher on knowledge. Conversely, private dentists (P = .003) and those referring patients to cardiologists before treatment (P = .003) scored lower. Dentists' knowledge of cardiovascular diseases in women was low; only those who believed women experience a greater risk of cardiac complications achieved a higher score. Approximately 90.1% wished to receive education regarding cardiac patient management. CONCLUSIONS: Knowledge of cardiac patient management was suboptimal in this study. Dentists perceived cardiac patients as difficult to manage, but wished to learn more regarding optimal management. Thus, postgraduate education programmes that promote optimal dental management strategies for cardiac patients are necessary.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".