Orthopaedic trauma care during a pandemic: initial responses in the United States and Canada
Bibliographic record
Abstract
As in other countries, COVID-19 had a significant impact on the delivery of Orthopaedic trauma care in North America. Both Canada and the United States had similar experiences and responses to the pandemic, while the burden of disease was significantly greater in the United States. There was significant uncertainty in the early phases of the pandemic, fueled by a lack of knowledge of the pathophysiology and spread of COVID-19, questions surrounding screening protocols, lack of guidelines for managing infected patients, and concern over limited supplies of personal protective equipment. As we gained knowledge and experience, changes were implemented to optimize the delivery of trauma care, some of which may have lasting effects. In this article, we share the experiences and lessons learned in Canada and the United States in response to the pandemic.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| 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".