The 1 Year Effect of COVID-19 on Plastic Surgery Trauma at a Level One Trauma Centre: A Retrospective Review
Bibliographic record
Abstract
Introduction: Since the onset of the COVID-19 pandemic, Canada has seen significant societal and health changes leading to the closures of many businesses and shifts in the daily activities of many Canadians. Despite these changes and a sharp drop in the number of patients attending emergency departments across British Columbia (BC), little change was noted in the use of protected plastic surgery trauma time at a level one trauma centre surveyed in BC. The purpose of this study was to analyze and compare the changes in plastic surgery-related trauma cases before and after the onset of the COVID-19 pandemic, both in etiology and case description. Methods: A retrospective medical chart review was performed, evaluating all of the participating surgeon's trauma slates in the year before and after April 1, 2020. Patient demographics, etiology, injury location, diagnosis, and surgical timing were all recorded and analyzed using an interrupted time-series statistical model. Results: No significant difference was observed in any of the recorded categories across the 2 time periods. Slight increases were noted in workplace injuries, assaults, and home-related machinery injuries. Conclusion: The lack of significant change in workplace injuries likely stemmed from the high number of factories and industrial plants present in our health region, as these jobs lacked the ability to work from home. The results of this study show that the demand for trauma-related plastic surgery care is independent of an overall decrease in hospital admissions and therefore should be planned and budgeted for accordingly.
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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 | 0.005 | 0.219 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".