Mortality and Morbidity in Office-Based General Anesthesia for Dentistry in Ontario
Bibliographic record
Abstract
Our objective was to estimate the prevalence of mortality and serious morbidity for office-based deep sedation and general anesthesia (DS/GA) for dentistry in Ontario from 1996 to 2015. Data were collected retrospectively in 2 phases. Phase I involved the review of incidents, and phase II involved a survey of DS/GA providers. In phase I, cases involving serious injury or death for dentistry under DS/GA, sourced from the Office of the Chief Coroner of Ontario and from the Royal College of Dental Surgeons of Ontario (RCDSO), were reviewed. Phase II involved a survey of all RCDSO-registered providers of DS/GA in which they were asked to estimate the number of DS/GAs administered in 2015 and the number of years in practice since 1996. Clinician data were pooled to establish an overall number of DS/GAs administered in dental offices in Ontario from 1996 to 2015. Prevalence was calculated using phase I (numerator) and phase II (denominator) findings. The estimated prevalence of mortality in the 20-year period from 1996 to 2015 was 3 deaths in 3,742,068 cases, with an adjusted mortality rate of 0.8 deaths per 1 million cases. The estimated prevalence of serious morbidity was 1 injury in 3,742,068 cases, which adjusts to a serious morbidity rate of 0.25 per 1 million cases. The mortality rate found in this study was slightly lower than those published by earlier studies conducted in Ontario. The risk of serious morbidity was found to be low and similar to other studies investigating morbidity in office-based dental anesthesia.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".