Impact of COVID-19 on hospital visits for non-traumatic dental conditions in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND AND RATIONALE: As general health care is publicly funded in Canada and oral health care is not, many people seek care from hospitals for their dental problems. This study assessed if the unprecedented times of Coronavirus disease (COVID-19) affected the hospital visits for dental emergencies, making disadvantaged populations further vulnerable for attendance of their dental problems. METHODS: Data from IntelliHealth Ontario for emergency department (ED) visits, day surgery visits, and hospitalizations associated with non-traumatic dental conditions (NTDCs) were retrieved for years 2016 to 2020 to assess trends before COVID-19 and changes, if any, for the year 2020. Trends by month, for the years 2019 and 2020, to make straight comparisons and understand the effects of lockdown in Ontario, was also analyzed. RESULTS: In the year 2020, there was a reduction of 40% in day surgeries, 21% in ED visits and 8% in hospitalizations compared to 2019. Stratified by month, largest reductions were observed in April 2020: 96% in day surgeries; 50% in ED visits; and 38% reductions in hospitalizations when compared to the same month of 2019. In May 2020, day surgeries and ED visits though remained reduced, hospitalization rates increased by 31%. CONCLUSION: Hospital EDs are inefficient avenues for handling dental emergencies. Nevertheless, they do remain a care setting that is sought by many for dental problems, and if the need for hospitalization and day surgery is there, this care setting is an important avenue for dentally related medical care. Perhaps unsurprisingly, COVID-19 has lessened the opportunity and capacity for such care. PRACTICAL IMPLICATIONS: Administrators and policy makers can utilize this information to strategize on augmenting community infrastructure for building more effective, and cost-efficient avenues of care for timely management of dental problems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".