Association of COVID-19 With Achieving Time-to-Surgery Benchmarks in Patients With Musculoskeletal Trauma
Bibliographic record
Abstract
Importance In response to the COVID-19 pandemic, many hospital systems were forced to reduce operating room capacity and reallocate resources. The outcomes of these policies on the care of injured patients and the maintenance of emergency services have not been adequately reported. Objective To evaluate whether the COVID-19 pandemic was associated with delays in urgent fracture surgery beyond national time-to-surgery benchmarks. Design, Setting, and Participants This retrospective cohort study used data collected in the Program of Randomized Trials to Evaluate Preoperative Antiseptic Skin Solutions in Orthopaedic Trauma among at 20 sites throughout the US and Canada and included patients who sustained open fractures or closed femur or hip fractures. Exposure COVID-19–era operating room restrictions were compared with pre–COVID-19 data. Main Outcomes and Measures Surgery within 24 hours after injury. Results A total of 3589 patients (mean [SD] age, 55 [25.4] years; 1913 [53.3%] male) were included in this study, 2175 pre–COVID-19 and 1414 during COVID-19. A total of 54 patients (3.1%) in the open fracture cohort and 407 patients (21.8%) in the closed hip/femur fracture cohort did not meet 24-hour time-to-surgery benchmarks. We were unable to detect any association between time to operating room and COVID-19 era in either open fracture (odds ratio [OR], 1.40; 95% CI, 0.77-2.55;P = .28) or closed femur/hip fracture (OR, 1.01; 95% CI, 0.74-1.37;P = .97) cohorts. In the closed femur/hip fracture cohort, there was no association between time to operating room and regional COVID-19 prevalence (OR, 1.07; 95% CI, 0.70-1.64;P = .76). Conclusions and Relevance In this cohort study, there was no association between meeting time-to-surgery benchmarks in either open fracture or closed femur/hip fracture during the COVID-19 pandemic compared with before the pandemic. This is counter to concerns that the unprecedented challenges associated with managing the COVID-19 pandemic would be associated with clinically significant delays in acute management of urgent surgical cases and suggests that many hospital systems within the US were able to effectively implement policies consistent with time-to-surgery standards for orthopedic trauma in the context of COVID-19–related resource constraints.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".