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Record W2286356945 · doi:10.3747/co.23.2810

A Canadian Perspective on the Use of Immunoglobulin Therapy to Reduce Infectious Complications in Chronic Lymphocytic Leukemia

2016· review· en· W2286356945 on OpenAlexaffvenueabout
S Lachance, Anna Christofides, J.K. Lee, Laurie H. Sehn, Bruce Ritchie, Chaim Shustik, Douglas A. Stewart, Cynthia L. Toze, Élie Haddad, Donald C. Vinh

Bibliographic record

VenueCurrent Oncology · 2016
Typereview
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsCentre Hospitalier Universitaire Sainte-JustineVancouver General HospitalUniversity of British ColumbiaBC Cancer AgencyCanadian Nutrition SocietyMcGill University Health CentreUniversity of AlbertaUniversité de Montréal
FundersCSL Behring
KeywordsMedicineHypogammaglobulinemiaChronic lymphocytic leukemiaImage-guided radiation therapyDiseaseIntensive care medicineLeukemiaImmunologyRadiation therapyInternal medicineAntibody

Abstract

fetched live from OpenAlex

Infections are a major cause of morbidity and mortality in patients with chronic lymphocytic leukemia (cll), who typically have increased susceptibility because of hypogammaglobulinemia (hgg) related to their disease and its treatment. Immunoglobulin replacement therapy (igrt) has been shown to reduce the frequency of bacterial infections and associated hospitalizations in patients with hgg or a history of infection, or both. However, use of igrt in cll is contentious. Studies examining such treatment were conducted largely before the use of newer chemoimmunotherapies, which can extend lifespan, but do not correct the hgg inherent to the disease. Thus, the utility of igrt has to be re-evaluated in the current setting. Here, we discuss the evidence for the use of igrt in cll and provide a practical approach to its use in the prevention and management of infections.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.986
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.279
GPT teacher head0.487
Teacher spread0.208 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations33
Published2016
Admission routes3
Has abstractyes

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