The impact of COVID‐19 in the management of AL amyloidosis and Immunoglobulin Deposition Disease: A single‐center experience
Bibliographic record
Abstract
INTRODUCTION: Patients with AL amyloidosis and immunoglobulin deposition diseases (IDD) are vulnerable during the COVID-19 pandemic due to the immune compromise from the plasma cell disorder and therapy-related immune defects. We describe a local experience in providing care for patients with AL amyloidosis and IDD. METHOD: Patient treatment and disease status since the beginning of the pandemic on March 11, 2020, as declared by WHO, were collected and analyzed. RESULTS: Ninety-six patients with AL amyloidosis and IDD were included. Four patients with IDD and 22 patients with systemic AL amyloidosis were receiving treatment during the pandemic. Since the pandemic, patients' treatments were discontinued if they achieved VGPR or better postinduction. Seven patients discontinued all treatment after achieving VGPR, and others required treatment modifications. 28 patients have been tested for COVID-19, and all tests have been negative. Three patients died since the pandemic, two from organ complications of systemic AL amyloidosis and one from an unrelated cause. CONCLUSION: The management of AL amyloidosis and IDD must be individualized on the clinical characteristics, centers' access to care under the pandemic restrictions, and the epidemiological aspects of the outbreak.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".