The Impact of the COVID-19 Pandemic on Cleft and Craniofacial-Related Surgeries at the Hospital for Sick Children
Bibliographic record
Abstract
INTRODUCTION: This study investigates the impact of the Coronavirus disease 2019 (COVID-19) pandemic on orthodontically associated cleft lip/palate and craniofacial surgeries, focusing on lip repairs, alveolar bone grafting (ABG), and orthognathic surgeries (OGS). METHODS: A retrospective chart review of 571 participants in a single hospital setting who underwent surgical lip repairs, ABG or OGS was conducted. Clinical data were retrieved from patients' electronic medical records. The data were used to quantify and compare the number of surgeries and surgical wait times pre-COVID versus post-COVID, using independent t tests and regression models, adjusting for various sociodemographic and clinical variables. RESULTS: The proportion of lip repairs and ABG performed was not significantly different between the pre-COVID and post-COVID (P=0.90 and 0.94, respectively); however, OGS significantly decreased post-COVID compared with pre-COVID (P<0.0001). Although no significant differences were found in surgical wait times for lip repairs across time periods (P=0.07), wait times for both ABG and OGS were significantly longer post-COVID compared with pre-COVID (both P<0.0001). Regression analyses showed similar associations after adjusting for several covariates. CONCLUSION: COVID-19 significantly impacted ABG and OGS procedures. Prolonged surgical wait times raise concerns regarding adverse outcomes on patients' physical and mental health. Emphasis should be placed on prioritizing essential procedures and implementing flexible scheduling. Further research is needed to assess the long-term effects of these delays on patient-centered outcomes.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".