Postoperative outcomes in surgical COVID-19 patients: a multicenter cohort study
Bibliographic record
Abstract
BACKGROUND: Data on postoperative outcomes of the COVID-19 patient population is limited. We described COVID-19 patients who underwent a surgery and the pandemic impact on surgical activities. METHODS: We conducted a multicenter cohort study between March 13 and June 192,020. We included all COVID-19 patients who underwent surgery in nine centres of the Province of Québec, the Canadian province most afflicted by the pandemic. We also included concomitant suspected COVID-19 (subsequently confirmed not to have COVID-19) patients and patients who had recovered from it. We collected data on baseline characteristics, postoperative complications and postoperative mortality. Our primary outcome was 30-day mortality. We also collected data on overall surgical activities during this first wave and during the same period in 2019. RESULTS: We included 44 COVID-19 patients, 18 suspected patients, and 18 patients who had recovered from COVID-19 at time of surgery. Among the 44 COVID-19 patients, 31 surgeries (71%) were urgent and 16 (36%) were major. In these patients, pulmonary complications were frequent (25%) and 30-day mortality was high (15.9%). This mortality was higher in patients with symptoms (23.1%) compared to those without symptoms (5.6%), although not statistically significant (p = 0.118). Of the total 22,616 cases performed among participating centres during the study period, only 0.19% had COVID-19 at the time of surgery. Fewer procedures were performed during the study period compared to the same period in 2019 (44,486 cases). CONCLUSION: In this Canadian cohort study, postoperative 30-day mortality in COVID-19 patients undergoing surgery was high (15.9%). Although few surgeries were performed on COVID-19 patients, the pandemic impact on surgical activity volume was important. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04458337 .
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".