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Record W1862199182 · doi:10.1155/2002/127953

Polyclonal Intravenous Immunoglobulin for the Prophylaxis and Treatment of Infection in Critically Ill Adults

2002· article· en· W1862199182 on OpenAlexafffundvenue
Kevin B. Laupland

Bibliographic record

VenueCanadian Journal of Infectious Diseases · 2002
Typearticle
Languageen
FieldMedicine
TopicStreptococcal Infections and Treatments
Canadian institutionsUniversity of Calgary
FundersCanadian Institutes of Health ResearchFondation pour la Recherche MédicaleBayer HealthCare
KeywordsMedicineSeptic shockIntensive care medicineSepsisPneumoniaRandomized controlled trialCritically illIncidence (geometry)ImmunologyInternal medicine

Abstract

fetched live from OpenAlex

Infection is a major cause of morbidity and mortality in critically ill patients. Despite advances in technology, its mortality rate has changed minimally over the past two decades, and new therapies are needed. Polyclonal intravenous immunoglobulin (IVIG) has been investigated both as a preventive and a treatment modality for sepsis and septic shock in critically ill adult patients. Prophylaxis with IVIG has been shown to reduce significantly the incidence of infection, particularly pneumonia, in selected postsurgical intensive care patients. However, it does not reduce mortality. The risk-benefit and cost effectiveness of this therapeutic intervention have not been determined, and its routine use is therefore not recommended. Treatment with IVIG has been shown in a number of small trials and a meta-analysis to reduce dramatically sepsis and septic shock mortality. However, a large, unpublished randomized trial has apparently shown no mortality benefit with this therapy. Despite limited evidence, IVIG has become the standard of care for the management of group A streptococcal toxic shock syndrome. At present, clinical equipoise exists for the use of IVIG in the treatment of sepsis and septic shock, and further study is needed.

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 score0.112
Threshold uncertainty score0.992

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 head0.013
GPT teacher head0.247
Teacher spread0.234 · 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

Citations15
Published2002
Admission routes3
Has abstractyes

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