Improving Patient Safety in Dentistry: A Systematic Review of Adverse Event Contributors
Bibliographic record
Abstract
Background: Adverse events in dental practice are a threat to patient’s safety, and hold practitioners accountable. Objective: This systematic review pinpoints and examines the factors causing adverse events in the dental setting. Methods: The Researcher ran a systematic search through Web of Science and Scopus databases, by focusing on studies from January 2010 up to January 2023. 20 studies were included and were evaluated with the Newcastle-Ottawa Scale. Results: Typical adverse events covered endodontic errors, along with pain, nausea, and problems during procedures. The major factors playing a role included; the dentist’s level of experience, complexity of the procedures, patient’s reactions to medications, and conditions unique to each patient. Notably, 70% of perforations are correlated to mistakes made by dental trainees. Conclusion: The majority of these dental adverse events could be avoided and are preventable with better training, setting protocols, and improved communication. These results point toward creating focused interventions to boost patient safety overall. Clinical Relevance: This review points out practical steps for decreasing adverse events in the dental practice, by stressing on the importance of strict infection control, ongoing education for practitioners, and updating policies.
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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 | 0.013 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".