Canadian Perspective on Managing Multiple Myeloma during the COVID-19 Pandemic: Lessons Learned and Future Considerations
Bibliographic record
Abstract
The coronavirus disease 2019 (covid-19) pandemic caused by the novel severe acute respiratory syndrome coronavirus 2 has necessitated changes to the way patients with chronic diseases are managed. Given that patients with multiple myeloma are at increased risk of covid-19 infection and related complications, national bodies and experts around the globe have made recommendations for risk mitigation strategies for those vulnerable patients. Understandably, because of the novelty of the virus, many of the proposed risk mitigation strategies have thus far been reactionary and cannot be supported by strong evidence. In this editorial, we highlight some of the risk mitigation strategies implemented at our institutions across Canada during the first wave of covid-19, and we discuss the considerations that should be made when managing patients during the second wave and beyond.
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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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.015 | 0.022 |
| Insufficient payload (model declined to judge) | 0.009 | 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".