Evolving management strategies for lymphomas during the COVID-19 pandemic
Bibliographic record
Abstract
The evolving CoViD-19 pandemic has raised unprecedented challenges for physicians who face significant constraints in medical resources and cancer therapies. The management of patients with lymphoma represents a unique challenge given the heterogeneity of the patient population and treatment goals as well as the myriad choices of therapy available to clinicians. Adaptation in clinical practice with the goal of maintaining appropriate continuity and quality of care while mitigating exposure risk has forced clinicians around the world to develop new standards of practice and can pose difficult ethical choices in vulnerable patient populations. Based on recommendations formulated by several medical groups and societies, this article provides an overview of the general and specific practical considerations that apply to the care of lymphoma patients during the outbreak. We hope to provide a practical framework to help guide physicians in their therapeutic choices and facilitate the ongoing management of this specific patient population.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".