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Record W2766623481 · doi:10.1186/s13223-017-0220-y

Initial intravenous immunoglobulin doses should be based on adjusted body weight in obese patients with primary immunodeficiency disorders

2017· article· en· W2766623481 on OpenAlexvenueno aff
Rohan Ameratunga

Bibliographic record

VenueAllergy Asthma and Clinical Immunology · 2017
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunodeficiency and Autoimmune Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsCommon variable immunodeficiencyMedicineImmunodeficiencyPrimary immunodeficiencyConcomitantWeight lossAntibodyBody weightObesityInternal medicineImmunologyGastroenterologyImmune system

Abstract

fetched live from OpenAlex

BACKGROUND: Immunoglobulin therapy plays a critical role in the treatment of immunodeficiency disorders as well as autoimmune and inflammatory conditions. In immunodeficient patients, there has been controversy whether initial loading doses of intravenous (IVIG) should be based on actual body weight or a calculated parameter such as adjusted body weight in obese patients. CASE PRESENTATION: I describe a patient with Common Variable Immunodeficiency disorder (CVID) who underwent bariatric surgery for morbid obesity. Her weight decreased by 50% to below her calculated ideal body weight (IBW) while her immunoglobulin requirement fell by approximately 20%. Her steady state serum IgG increased from approximately 7 g/l to 11.7 g/l concomitant with weight loss. CONCLUSIONS: I present this observation as support for the recommendation that initial loading doses of SCIG/IVIG in immunodeficiency should be based on adjusted body weight (AjBW) and not actual body weight in obese patients. This has important fiscal implications for treating obese patients with immunodeficiency disorders.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.760
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.276
Teacher spread0.260 · 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; both teacher heads agree on what is shown here.

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

Citations18
Published2017
Admission routes1
Has abstractyes

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