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Record W3106232974 · doi:10.1111/ejh.13552

The impact of COVID‐19 in the management of AL amyloidosis and Immunoglobulin Deposition Disease: A single‐center experience

2020· article· en· W3106232974 on OpenAlexaff
Holly Lee, Jason Tay, Peter Duggan, Sylvia McCulloch, Paola Neri, Nizar J. Bahlis, Víctor H. Jiménez‐Zepeda

Bibliographic record

VenueEuropean Journal Of Haematology · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAmyloidosis: Diagnosis, Treatment, Outcomes
Canadian institutionsOntario Institute for Cancer ResearchBaker Hughes (Canada)
Fundersnot available
KeywordsMedicineAmyloidosisPandemicOutbreakAL amyloidosisCoronavirus disease 2019 (COVID-19)DiseaseIntensive care medicineInternal medicinePediatricsImmunologyAntibodyInfectious disease (medical specialty)Pathology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.295

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.296
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2020
Admission routes1
Has abstractyes

Explore more

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