Where Enhanced Recovery after Surgery (ERAS) Protocols Meet the Three Major Current Pandemics: COVID-19, Obesity and Malignancy
Bibliographic record
Abstract
The outbreak of the SARS-CoV-2 (COVID-19) pandemic has transformed the provision of medical services for both patients that receive care for COVID-19 and for those that need care either for benign diseases, including obesity, or for malignancies, such as gynecological cancer. In this perspective article, we focus on the association among three major worldwide health issues and how ERAS protocols can potentially provide optimal management of patients with obesity and malignancy during the COVID-19 pandemic, with special attention to patients who required surgery for gynecologic oncology. A thorough search of the literature on the respective topics was performed. Patients with malignancy and obesity presented with increased vulnerability to COVID-19 infection. However, the management of their disease should not be withheld. Protective measures should be established to reduce exposure of patients with oncological diseases to SARS-CoV-2 while simultaneously enabling their access to vaccination. Since ERAS protocols have proved to be efficient in many surgical fields, including gynecologic oncology, general surgery, and orthopedics, we strongly believe that ERAS protocols may play a significant role in this effort. The end of the COVID-19 pandemic cannot be accurately predicted. Nevertheless, we have to ensure the appropriate and efficient management of certain groups of patients.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".