An Expert Opinion Regarding the Management of Lower Gastrointestinal Cancers During COVID-19 Outbreak in Iran: A Brief Report
Bibliographic record
Abstract
Lower gastrointestinal cancer patients, like other immunosuppressed patients, are vulnerable to develop more severe infections. Iran is one of the countries with a high incidence of COVID-19, and some modifications are needed to adjust international protocols to deal with this pandemic. Therefore, our cancer institute has implemented some changes in the current treatment guidelines. In each specialty, all members agreed to choose the minimal intervention. The members know that some recommendations may interfere with the routine best-practice recommendations and decrease the quality measures in the patient's outcome. Therefore, these recommendations are valid just in the epidemic COVID-19 situation in the country. According to the consensus of colorectal and cancer surgery professors, if a patient is a new case of rectal cancer, he or she should be referred to undergo neoadjuvant chemoradiotherapy. But if the patient comes eight to twelve weeks after receiving chemoradiotherapy in accordance with the COVID-19 epidemic phase, it may be possible to delay surgery. A stent can be implanted in a patient with rectal cancer who presents with obstructive symptoms, and surgery can be postponed until resolving the crisis of the COVID-19 epidemic or its downward trend. For colon cancer, we will request a thoracic spiral CT scan. If there was no evidence of pulmonary involvement with COVID-19, the patient would undergo open surgery considering the patient's health and protection tips.
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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 | 0.014 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".