Impact of COVID-19 Pandemic on Trauma CT Imaging
Bibliographic record
Abstract
Purpose. The goal of this study was to understand the impact of COVID-19 pandemic and associated lockdown measures on the volume, rate, and type of trauma presenting to the emergency department (ED) by using trauma-initiated CT studies to capture patient data. Materials and Methods. We performed a retrospective observational study comparing patients undergoing CT scans for trauma during the 1st and 2nd lockdown periods compared to corresponding prepandemic months. During two lockdown periods, public places such as restaurants, libraries, parks, and shops across the province were shut down. Government-led messaging advised that people should stay at home and practice social distancing. The rate of trauma-initiated CT scans and the proportion of different types of traumas were compared between time periods. Results. There was no significant difference in overall trauma-initiated CT scans between the prepandemic and pandemic levels. Motor vehicle collision (MVC) cases decreased from 18.2% to 15.6% during the first lockdown period ( p = 0.049) and also reduced from 29.1% to 25.2% during the second lockdown period ( p = 0.013). Trauma from falls increased from 19.1% to 27.5% ( p = 0.036) during the first lockdown, despite no significant change during the 2nd lockdown. Furthermore, the percentage of stab injuries increased from 25.0% to 38.9% while blunt trauma decreased from 68.5% to 54.3% during two lockdowns ( p = 0.015). Conclusion. The total number of trauma-initiated CT scans did not significantly decrease during the lockdown periods. Stabbings and falls increased during lockdown periods while MVCs and blunt trauma decreased.
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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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".