Surgical Site Infections During the Pandemic: The Impact of the “COVID Bundle”
Bibliographic record
Abstract
BACKGROUND: A reduction in surgical site infections (SSIs) has been reported in several discrete patient populations during the COVID-19 pandemic. Herein, this study evaluates the impact of the COVID-19 pandemic on SSI in a large patient cohort incorporating multiple surgical disciplines. We hypothesize that enhanced infection control and heightened awareness of such measures is analogous to an SSI care bundle, the hypothetical "COVID bundle", and may impact SSI rates. METHOD: Data collected for the American College of Surgeons National Surgical Quality Improvement Program between January 1, 2015, and April 1, 2021, were retrospectively analyzed. SSI rates were compared among time-dependent patient cohorts: Cohort A (pre-pandemic, N = 24,060, 87%) and Cohort B (pandemic, N = 3698, 13%). Time series and multivariable analyses predicted pre-pandemic and pandemic SSI trends and tested for association with timing of surgery. RESULTS: The overall SSI incidence was reduced in Cohort B versus Cohort A (2.8% vs. 4.5%, p < 0.001). Multivariable analysis indicated a downward SSI trend before pandemic onset (IRR 0.997, 95% CI 0.994, 1). At pandemic onset, the trend reduced by a relative factor of 39% (IRR 0.601, 95% CI 0.338, 1.069). SSI then trended upward during the pandemic (IRR 1.035, 95% CI 0.965, 1.111). SSI rates significantly trended downward in general surgical patients at pandemic onset (IRR 0.572, 95% CI 0.353, 0.928). CONCLUSION: Although overall SSI incidence was reduced during the pandemic, a statistically significant decrease in the predicted SSI rate only occurred in general surgical patients at pandemic onset. This trend may suggest a positive impact of the "COVID bundle" on SSI rates in these patients.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".