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Record W2944731734 · doi:10.1111/ejh.13244

Deferiprone exerts a dose‐dependent reduction of liver iron in adults with iron overload

2019· article· en· W2944731734 on OpenAlexaff
Andrew Binding, Richard Ward, George Tomlinson, Kevin H.M. Kuo

Bibliographic record

VenueEuropean Journal Of Haematology · 2019
Typearticle
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsDeferiproneMedicineGastroenterologyAdverse effectInternal medicineNeutropeniaCohortThalassemiaChemotherapy

Abstract

fetched live from OpenAlex

OBJECTIVE: While doses of deferiprone up to 75 mg/kg/d have been demonstrated to be effective in cardiac iron removal, their efficacy in the reduction of liver iron has been equivocal. The aim of this study was to evaluate the effect of deferiprone dose on liver iron concentrations in adult iron overload patients. METHODS: A single-centered, retrospective, cohort observational study was conducted involving 71 patients exposed to deferiprone doses up to 113 mg/kg/d between January 2009 and June 2015 for a median of 33 months. RESULTS: At the end of the study period, liver iron measured by R2 MRI was reduced by a mean 1.7 mg/g dw. A dose effect was observed, with incremental reductions of 2.8 mg/g dw in end of study LIC for every 10 mg/kg/d higher dose of deferiprone (P < 0.001). A dose effect was also observed in end of study ferritin and cardiac iron concentration measured by T2* MRI (P < 0.0001 and P = 0.048, respectively). No associations between adverse effects and deferiprone dose were observed, but there was a trend toward higher rates of agranulocytosis at higher doses and two of three hereditary hemochromatosis patients developed this complication. CONCLUSION: The present study failed to demonstrate that the use of deferiprone at >90 mg/kg/d was associated with increased risk of agranulocytosis or neutropenia, but did demonstrate more effective liver iron control in iron overload patients.

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 score0.195
Threshold uncertainty score0.378

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.006
GPT teacher head0.208
Teacher spread0.202 · 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

Citations21
Published2019
Admission routes1
Has abstractyes

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