COVID-19 orthopaedic trauma volumes: a Canadian experience during lockdown and staged reopening
Bibliographic record
Abstract
Abstract Objectives: The aim of this study is to report the impact of public health measures (PHM), including their relaxation, on surgical orthopaedic trauma volumes. We hypothesize an initial reduction in orthopaedic trauma volumes during lockdown followed by a surge as Stages 1 and 2 of reopening progressed in Summer 2020. Methods: All unscheduled surgical orthopaedic trauma cases from March through August were retrospectively reviewed in Calgary, Alberta, Canada, from 2008 to 2020. Trauma volumes from 2008 to 2019 were used to model expected 2020 volumes, and multivariable Poisson regression was used to determine the effect of PHM on orthopaedic trauma volumes. Results: A total of 22,331 trauma orthopaedic surgeries were included. During lockdown, there was a significant decrease in trauma volume compared with expected (−14.2%, −25.7 to −10.5%, P < .001) and there were significantly fewer ankle fractures (−17.8%, −30.9 to −2.2%, P = .027). During reopening Stage 2, there was a significant increase in trauma volume (+8.9%, +2.2 to +16.1%, P = .009). There was no change in the incidence of polytrauma, hip fracture, or wrist fracture during the pandemic. Conclusions: This study provides the first report of a surge in trauma volumes as PHM are relaxed during the COVID-19 pandemic. The ability to predict decreases in trauma volumes with strict PHM and subsequent surges with reopening can help inform operating room time management and staffing in future waves of COVID-19 or infectious disease pandemics. Level of Evidence: Prognostic – Level III
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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 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".