Impact of COVID-19 on the Diagnosis and Management of Multiple Myeloma: Experience from a Canadian Center
Bibliographic record
Abstract
BACKGROUND: The impact of coronavirus disease-19 on the management of multiple myeloma (MM) has been recognized. However, the real effect on clinical outcomes remains poorly understood. OBJECTIVE: We describe a local experience of the management of MM patients and report their outcomes during the current pandemic. METHODS: All consecutive symptomatic MM patients seen at our center since 03/20 were evaluated. RESULTS: A cohort of 156 patients diagnosed from 01/19 to 12/20 was analyzed to interrogate differences in presentation patterns. A total of 553 MM patients were seen and/or treated at Tom Baker Cancer Center in the year of 2020. From those, 47.1% (n = 261) were tested for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Sixteen patients tested positive and data are presented. In addition, a decrease of 21.7% in the rate of new smoldering MM/MM diagnosis was observed in 2020 as compared to 2019. Further, an increase in deaths was also observed in 2020. CONCLUSIONS: Our study confirms an increase lethality for MM patients infected with SARS-CoV-2. A balance between safety and need for cancer control should be emphasized.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".