Managing hematological cancer patients during the COVID-19 pandemic: an ESMO-EHA Interdisciplinary Expert Consensus
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has created enormous challenges for the clinical management of patients with hematological malignancies (HMs), raising questions about the optimal care of this patient group. METHODS: This consensus manuscript aims at discussing clinical evidence and providing expert advice on statements related to the management of HMs in the COVID-19 pandemic. For this purpose, an international consortium was established including a steering committee, which prepared six working packages addressing significant clinical questions from the COVID-19 diagnosis, treatment, and mitigation strategies to specific HMs management in the pandemic. During a virtual consensus meeting, including global experts and lead by the European Society for Medical Oncology and the European Hematology Association, statements were discussed and voted upon. When a consensus could not be reached, the panel revised statements to develop consensual clinical guidance. RESULTS AND CONCLUSION: The expert panel agreed on 33 statements, reflecting a consensus, which will guide clinical decision making for patients with hematological neoplasms during the COVID-19 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 | 0.091 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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".