Mortality and Morbidity Associated With Out-of-Hospital Deep Sedation and General Anesthesia for Dental Treatment: A 36-Year Retrospective Study in British Columbia, Canada (1984-2019)
Bibliographic record
Abstract
Objective: Previous studies in Canada suggest that mortality and serious morbidity during deep sedation and/or general anesthesia (DS/GA) for dentistry in out-of-hospital facilities are low. The purpose of this study was to estimate the period prevalence of mortality and serious morbidity associated with outpatient DS/GA for dentistry in British Columbia, Canada. Methods: Events were identified by retrospectively searching the Chief Coroner of British Columbia database from 1987 to 2019, the College of Dental Surgeons of British Columbia database from 1984 to 2019, and gray literature from 1984 to 2019. A survey of DS/GA providers was conducted to estimate the number of DS/GA procedures provided. Results: A total of 3 linked mortality events in which anesthesia could not be ruled out as a contributing factor were identified. No cases of serious morbidity met the inclusion criteria for the study. An estimated 1,019,853 out-of-hospital DS/GA procedures for dental treatment were provided during the study period. This study estimated a period prevalence of mortality and serious morbidity of 2.94 per 1 million out-of-hospital DS/GA procedures for dental treatment when administered by qualified providers over the 36-year study period. Conclusion: These findings suggest that the provision of out-of-hospital DS/GA for dental treatment in British Columbia carries a low risk of mortality or serious morbidity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".