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Record W3202324403 · doi:10.1080/10428194.2021.1961233

Infections in secondary immunodeficiency patients treated with Privigen<sup>®</sup>or Hizentra<sup>®</sup>: a retrospective US administrative claims study in patients with hematological malignancies

2021· article· en· W3202324403 on OpenAlexaff
Rajiv Mallick, Victoria Divino, B. Douglas Smith, Stephen Jolles, Mitch DeKoven, Donald C. Vinh

Bibliographic record

VenueLeukemia & lymphoma/Leukemia and lymphoma · 2021
Typearticle
Languageen
FieldMedicine
TopicPneumocystis jirovecii pneumonia detection and treatment
Canadian institutionsMcGill University Health Centre
FundersCSL Behring
KeywordsRetrospective cohort studyMedicineInternal medicine

Abstract

fetched live from OpenAlex

B cell-derived lymphoproliferative disorders are associated with secondary immunodeficiency (SID); some patients require immunoglobulin replacement therapy (IgRT) to mitigate infections. Using IQVIA’s PharMetrics® Plus database, patients with SID who received IgPro10/IgPro20 in the 12 months post-diagnosis (IgRT users) were matched to patients with SID not receiving IgRT (non-IgRT users). The risk of severe infection was compared using within-patient change from baseline to follow-up as well as between cohorts. Overall, 277 IgRT users were matched to 1019 non-IgRT users. Before IgRT, more IgRT users experienced any bacterial infection (88.4% vs. 72.9%; p<.0001) or ≥1 severe bacterial infection (SBI) (42.2% vs. 31.8%; p=.0011) vs. non-IgRT users. During follow-up, risk of SBI among IgRT users (21.7%) reached parity with non-IgRT users (21.2%). IgRT was associated with a reduction in SBIs to levels comparable with the lower ‘baseline infection risk’ of non-IgRT users. These criteria help define SID patients who may benefit from IgRT.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.233
Teacher spread0.224 · 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 source (direct Gemma or distilled Codex), 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

Citations13
Published2021
Admission routes1
Has abstractyes

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