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Prospective audit of adverse reactions occurring in 459 primary antibody-deficient patients receiving intravenous immunoglobulin

2003· article· en· W2015912396 on OpenAlexfundno aff
V Brennan, Nicola Salome-Bentley, Helen Chapel, J. Abbey, Tracey Askew, Sheila Cochrane, Claire Fletcher, Claire E. Bennett, Cilla Freud, Annie Ryan, Mary Haines, Pauline Powell, Delene Saunderson, Jan Short, John Toolan, D. Hendry, Ann Wilkes

Bibliographic record

VenueClinical & Experimental Immunology · 2003
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunodeficiency and Autoimmune Disorders
Canadian institutionsnot available
FundersHospital for Sick Children
KeywordsAntibodyMedicineImmunologyProspective cohort studyAdverse effectIntravenous ImmunoglobulinsAuditInternal medicine

Abstract

fetched live from OpenAlex

Intravenous immunoglobulin (IVIG) is used as the standard replacement therapy for patients with primary antibody deficiencies. A previous study of adverse reactions in patients self-infusing at home over 1 year showed an overall reaction rate of 0.7%. A larger prospective study is reported here, involving a greater number of immunology centres and including children and adults who received infusions from medical or nursing staff as well as those self-infusing. Four hundred and fifty-nine patients were entered into this study and 13 508 infusions were given. The study showed that no severe reactions occurred and the reaction rate was low at 0.8%. This figure could have been lower, 0.5%, if predisposing factors responsible for some reactions had been considered before infusion. In conclusion, the study shows the importance of ongoing training for patients and staff to recognize the predisposing factors to prevent avoidable reactions. Because none of these reactions were graded as severe, the present guidance to prescribe self-injectable adrenaline for patients infusing outside hospital should be reviewed.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.014
GPT teacher head0.309
Teacher spread0.295 · 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

Citations128
Published2003
Admission routes1
Has abstractyes

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