Monoclonal Gammopathy of Undetermined Significance Clinic during the Coronavirus Disease-19 Pandemic: Caring for the Vulnerable in an Academic Medical Center
Bibliographic record
Abstract
BACKGROUND: Patients with monoclonal gammopathy of undetermined significance (MGUS) have clinical features including older age, presence of medical comorbidities, susceptibility to infections, and thrombotic tendencies which are relevant when assessing their risk during the coronavirus disease (COVID-19) pandemic. OBJECTIVE: To study the vulnerability of patients with MGUS during the COVID-19 pandemic, we assessed the local management of MGUS patients and their clinical outcomes. METHODS: Retrospective chart reviews were performed for all patients with MGUS seen at a university medical center clinic (2014-2020). RESULTS: A total of 228 MGUS patients were included; 211 patients are alive, 7 patients died before the pandemic, and 10 patients died since the pandemic declaration. The mean age and the overall survival (OS) of the patients who died before versus during the pandemic were 83.0 versus 75.2 years, p = 0.4, and OS 40.6 versus 53.2 months, p = 0.3, respectively. One patient died of COVID-19. Nine patients had venous thromboembolisms (VTE), all of which occurred before the pandemic onset. CONCLUSIONS: There were no significant differences found in the mean age or OS of the MGUS patients who died before versus after the pandemic onset. An increase in VTE rates was not seen. Study results are limited by small patient numbers.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".