COVID-19 Pneumonia on Post-Operative Day 2 after Esophagectomy: Performing Esophago-Gastric Junction Cancer Surgery during the SARS-Cov-2 Second Wave
Bibliographic record
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has had a substantial impact on the provision of medical healthcare. Due to an increased risk of severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) transmission, elective surgical treatment has been suspended in many centers. The effects of COVID-19 in the early post-operative period after esophagectomy remains unknown. In this report, we present three cases of patients diagnosed with esophago-gastric junction cancer who were scheduled for elective esophagectomy with a curative intention during second wave of COVID-19 pandemic in a single high-volume tertiary center. Despite all available safety measures, one of the patients developed COVID-19 pneumonia on post-operative day two, leading to an impaired respiratory function and increased pleural fluid collection from the chest tube, resulting in a prolonged time of hospital stay. Finding a good balance between the COVID-19-related perioperative risks and consequences of delaying surgical treatment in patients diagnosed with esophago-gastric cancer is a challenge. In order to achieve the best possible outcome, care must be taken to ensure availability of necessary treatment options and to reduce the risk of SARS-Cov-2 transmission perioperatively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".