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Record W4309613501 · doi:10.3390/hematolrep14040048

Cutaquig® Is Well Tolerated in Immunodeficient Patients Who Did Not Tolerate Other Subcutaneous Immunoglobulin Products

2022· article· en· W4309613501 on OpenAlexaffabout
Sydney Brownlee, Crystal Allen, Mohammed F. Kana’an, D. William Cameron, Juthaporn Cowan

Bibliographic record

VenueHematology Reports · 2022
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunodeficiency and Autoimmune Disorders
Canadian institutionsUniversity of OttawaOttawa Hospital
Fundersnot available
KeywordsMedicineTolerabilityAdverse effectQuality of life (healthcare)Prospective cohort studyInternal medicineUrinary systemCohort

Abstract

fetched live from OpenAlex

Objective: Subcutaneous immunoglobulin (SCIG) treatment is generally tolerable, but some patients may experience adverse events to one or more SCIG products. We investigated whether 16.5% Cutaquig® treatment offered a tolerable and safe alternative treatment for immunodeficient patients. Methods: A one-year prospective cohort study was conducted at a single center in Ottawa, Canada. Adult immunodeficient patients who reported previous intolerability, adverse events, or other difficulty to other 20% SCIG product(s) were recruited to start on 16.5% Cutaquig®. Treatment tolerability, safety, and quality of life were observed and described. Results: Seven out of ten patients tolerated Cutaquig®. There were no serious or severe adverse events related to the treatment. Three moderate infections were reported (two urinary tract infections and one injection site infection). The mean serum IgG level at the end of the study was comparable to baseline levels recorded before the study: 9.6 ± 4.5 vs. 7.6 ± 4.3 g/L, p = 0.07. The overall health and health domain changes in the SF-36 and quality of life tests using the EQ visual analog scale improved by 21.5% (p = 0.38), 16.7% (p = 0.29), and 7.7% (p = 0.23), respectively. Conclusions: Cutaquig® may be used as an alternative treatment option for patients who did not tolerate 20% SCIG products.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.576
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.007
GPT teacher head0.207
Teacher spread0.200 · 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 designBench or experimental
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

Citations3
Published2022
Admission routes2
Has abstractyes

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