Pattern of Dental Emergencies at a Pediatric Tertiary Care Hospital during the COVID-19 Pandemic: A Retrospective Study.
Bibliographic record
Abstract
BACKGROUND: The onset of the COVID-19 pandemic and government restrictions affecting dental health care professionals had an impact on pediatric dental emergency trends. The purpose of this study was to describe the effect of the COVID-19 pandemic on the characteristics, outcomes and management of pediatric dental emergencies in a single tertiary care hospital. METHODS: A retrospective review of children presenting to Montreal Children's Hospital for dental emergencies before and during the pandemic was conducted. Data collected included children's demographic characteristics, type of emergency visit, clinical signs and symptoms, as well as emergency management. For the pandemic period, data regarding patient symptoms of COVID-19 infection were also noted. RESULTS: Of the 2745 pediatric dental emergencies included, 1336 (48.7%) occurred in 2019 and 1409 (51.3%) in 2020. During the first wave of COVID-19, the number of pediatric dental emergencies increased by 21% over pre-pandemic levels. A significant increase in the number of emergencies associated with dental infection was noted during the pandemic period (p = 0.04). A significant increase in the number of visits not receiving effective immediate treatment (p < 0.01) occurred during the early pandemic period. CONCLUSION: Our study shows a significant increase in the rates of dental emergencies and acuity of dental conditions during the first wave of the pandemic. Increased public health measures and adaptation to this ongoing public health crisis are important to ensure continued high-quality dental care for pediatric patients.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".