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A Retrospective Case Review of Adverse Drug Reactions Using Intravenous Iron in Non-Hemodialysis Patients.

2004· article· en· W2547835656 on OpenAlexaffabout
Cyrus C. Hsia, Katrina Ormond, Anagyros Xenocostas, Ian Chin‐Yee

Bibliographic record

VenueBlood · 2004
Typearticle
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsMedicineIron sucroseHemodialysisIntravenous ironAdverse effectIncidence (geometry)Intravenous therapyAdverse drug reactionRetrospective cohort studySurgeryPediatricsInternal medicineIron deficiencyAnemiaDrugPharmacology

Abstract

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Abstract Background: Intravenous iron therapy is commonly used in hemodialysis patients but has not been well studied in other patient populations. Intravenous iron has many documented adverse drug reactions and the types and incidence of reactions differ based on the type of intravenous iron therapy used. In Canada, two forms of iron therapy are currently being used: Iron dextran marketed as Infufer or DexIron and iron sucrose marketed as Venofer are available. The general consensus in the literature is that the incidence of serious adverse reactions is relatively low approximately 0.6–0.7% with intravenous iron dextran. The objective of this retrospective chart review was to observe recorded adverse events of iron dextran (Infufer) and iron sucrose (Venofer) in our own non-hemodialysis patients. Methods: 240 non-hemodialysis adult outpatient charts were reviewed. Iron dextran (Infufer) or iron sucrose (Venofer) infusions were recorded from July 20, 2000 to July 13, 2004. For each chart, the patient age, sex, date of birth, past medical history, medications and allergies were recorded. The type of intravenous iron, if premedication was used, and a description of any reaction if it occurred was also recorded. Each adverse reaction was graded on causality, severity, and system classification based on WHO standards done by two investigators virtually blinded to the type of iron therapy used. Results: Of the 240 patient charts reviewed, there were a total of 403 intravenous iron infusions given within the study period. The age of the patients ranged from 19 to 91 with mean age 60.3 +/− 16.3. The majority of our patients were end-stage renal disease peritoneal dialysis patients 187 (78%). 11 (5%) had a history of connective tissue disease or vasculitis, 15 (6%) had a history of asthma, and 84 (35%) used an angiotension converting enzyme inhibitor (ACEI). Only 17 patients (38 total infusions) received premedication. The total number of adverse events of all descriptions was 103 (26%) of the 403 total intravenous iron infusions. This was equally distributed to males 40 out of 156 (26%) and females 63 out of 247 (26%). Of the 365 intravenous therapies not given premedication there were 89 (24%) adverse events. The total number of "certain severe allergic" reactions (CSAs) was 25/403 (6%). In iron dextran (Infufer) a total of 77/295 (26%) ADRs were noted and CSAs of 23/295 (8%). In iron sucrose (Venofer) there was a total of 26/105 (25%) ADRs and 2/105 (2%) CSAs. Of the 295 intravenous iron dextran infusions, 209 had a test dose given. Of these 209, there were 60 ADRs - 25 during the test dose (12%) and 35 (17%) after the test dose. Conclusions: Adverse events and CSAs in our adult outpatient non-hemodialysis patients receiving intravenous iron therapy with either iron dextran (Infufer) and iron sucrose (Venofer) are much higher than previously reported in the literature. There are more ADRs and CSAs in the iron dextran group than the iron sucrose group. Premedication did not appear to reduce ADRs. Having a normal test dose did not preclude to getting ADRs afterwards.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.008
GPT teacher head0.251
Teacher spread0.243 · 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 designCase report
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".

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Citations1
Published2004
Admission routes2
Has abstractyes

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