Postoperative outcomes in surgical COVID-19 patients: a multicenter cohort study
Bibliographic record
Abstract
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".