Patient Safety Incidents and Adverse Events in Ambulatory Dental Care: A Systematic Scoping Review
Bibliographic record
Abstract
BACKGROUND: There have been efforts to understand the epidemiology of iatrogenic harm in hospitals and primary care and to improve the safety of care provision. There has in contrast been very limited progress in relation to the safety of ambulatory dental care. OBJECTIVES: To provide a comprehensive overview of the range and frequencies of existing evidence on patient safety incidents and adverse events in ambulatory dentistry. METHODS: We searched MEDLINE and EMBASE for articles reporting events that could have or did result in unnecessary harm in ambulatory dental care. We extracted and synthesized data on the types and frequencies of patient safety incidents and adverse events. RESULTS: Forty articles were included. We found that the frequencies varied very widely between studies; this reflected differences in definitions, populations studied, and sampling strategies. The main 5 PSIs we identified were errors in diagnosis and examination, treatment planning, communication, procedural errors, and the accidental ingestion or inhalation of foreign objects. However, little attention was paid to wider organizational issues. CONCLUSIONS: Patient safety research in dentistry is immature because current evidence cannot provide reliable estimates on the frequency of patient safety incidents in ambulatory dental care or the associated disease burden. Well-designed epidemiological investigations are needed that also investigate contributory factors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.022 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".