The Effect of Lockdown on the Number of Trauma Cases and the Pattern of Injury in a Trauma Centre in Dubai During the Month of Lockdown and Comparing it to the Pre-COVID Time
Bibliographic record
Abstract
Background: Due to the COVID-19 pandemic, full time lockdown was applied in many countries around the world. Published literature shows that the risk of suicide and assaults has increased but the risk of motor vehicle accidents has reduced. In this research, we want to study the impact of lockdown during the COVID-19 pandemic on the trauma cases who presented to the trauma cen-ter. Method: This retrospective cross-sectional study retrieved all the trauma cases who presented to the Emergency department in Rashid hospital during April 2019 and April 2020 from the patients' electronic records system. We included all the trauma cases who fell in the Canadian Triage and Acuity Scale (CTAS) of T1-T3 only. Results: Total of 3,268 trauma cases were studied. The number of cases in April 2019 was double the cases in 2020. However, the pattern of injuries was variable in the two years. There were high-er rates of motor vehicle accidents in non-lockdown month than the lockdown month (10.5% and 6.5%, respectively) (p-value
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.004 | 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".