Impact of COVID‐19 Pandemic on Readmission Rates Following Colorectal Surgery: A Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic placed increased pressure to discharge patients early; this could have resulted in rushed discharges requiring patients to return to hospital. The impact of the pandemic on readmission after colorectal surgery is unknown. METHODS: The National Surgical Quality Improvement Program (ACS-NSQIP) database was used to compare patients undergoing elective colorectal surgery in 2019 and 2020, prior to and during the COVID-19 pandemic. Multivariable logistic regression was used to examine variables associated with readmission. Propensity score matching was then used to compare patients in the pre-pandemic and pandemic cohorts. RESULTS: A total of 72,874 colorectal cases were included. There were 17.7% less cases in 2020. Rate of readmission was similar in both groups (9.6% vs. 9.4%). There were fewer patients discharged to a facility such as nursing facility or rehabilitation center in 2020, with more patients discharged home. Year was not associated with readmission on multivariable analysis. In the matched cohort, readmission rates did not differ (9.7% vs. 9.3% p = 0.129) nor did mortality (0.8% vs. 0.8% p = 0.686). CONCLUSIONS: No difference in readmission rates before or during the COVID-19 pandemic was observed; suggesting increased pressure to keep patients out of hospital in the COVID-19 pandemic did not result in patients being rushed home requiring repeat admission. More patients were discharged home with fewer to rehabilitation or nursing facilities in 2020, suggesting success with avoiding transitional services in the right setting.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.000 | 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".