The “Super Green Pathway”; What Have We Learned So Far?
Bibliographic record
Abstract
Background The coronavirus disease (COVID-19) had so far claimed more than 600 000 lives worldwide. Many urgent and elective surgeries were postponed to cope with the pandemic, with the latest data found a substantial postoperative mortality risk (25.6%, 18.9%) after an emergency and elective surgery, respectively. Our institution was one of the first few in the country to offer essential elective surgery using a “COVID-free” designated site during the start of the pandemic. This study aims to analyze the clinical outcomes of patients who underwent essential elective procedures during the virus outbreak in the UK. Methods Retrospective analysis of outcomes of all patients who had undergone urgent elective and cancer surgery, from 30th March 2020 to 21st May 2020, using an implemented “Super Green Pathway.” The primary endpoints were 30 days mortality and COVID-related morbidities, and the secondary end-points were surgically related complications and oncological outcomes. Results A total of 92 patients (Male: 45%; Female: 55%) across 5 surgical specialties were identified. There was no record of mortality in our cohort. Only 1 patient was tested positive for SARS-CoV-2, 18 days after the initial operation without any pulmonary complications. There were 7 postoperative surgical complications managed at the acute hospital site. The waiting time for surgery ranges from 6 to 191 days, mean of 30 days, and a median of 23 days. Conclusion It is possible to mitigate the high mortality risk of post-operative complications associated with COVID-19, with no delay to essential surgeries for cancer patients, thus delivering safe practice during the pandemic.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".