Modifications in Breast Cancer Guidelines in COVID-19 Pandemic; An Iranian Consensus
Bibliographic record
Abstract
Background: In March 2020, the World Health Organization declared the novel COVID-19 infection a pandemic. Among high-risk patients infected by the virus, breast cancer patients are vulnerable to present more severe infections. Iran is among the countries with a high incidence of COVID-19 , and most of the routine activities of medical centers are affected by the epidemic disease. Thus, there is a need to make some modifications to international protocols for dealing with breast cancer in the affected countries. Methods: The headings of breast cancer management protocols have been discussed among the university-affiliated professors in different disciplines involved in breast cancer management. The discussions were done through a “WhatsApp” group considering the tiles and the latest news about COVID-19. Under each title, we provide the consensus of all members in the related disciplines. Recommendations and Conclusion: In each specialty, all members agreed to choose minimal intervention. The modifications aim to reduce the workload of the medical centers as well as to provide the least interface of the patients with the medical centers. 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 epidemic COVID-19 situation in the country.
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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 | 0.059 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.016 | 0.024 |
| Insufficient payload (model declined to judge) | 0.004 | 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".