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Oral deferiprone for iron chelation in people with thalassaemia

2007· reference-entry· en· W4244128297 on OpenAlexaff
David J. Roberts, Susan J Brunskill, Carolyn Dorée, Senani Williams, Jo Howard, Chris Hyde

Bibliographic record

VenueCochrane Database of Systematic Reviews · 2007
Typereference-entry
Languageen
Field
Topic
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsDeferiproneMedicineDeferoxamineClinical trialThalassemiaChelation therapyBeta thalassemiaRandomized controlled trialIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Thalassaemia major is a genetic disease characterised by a reduced ability to produce haemoglobin. Management of the resulting anaemia is through transfusions of red blood cells. Repeated transfusions result in excessive accumulation of iron in the body (iron overload), removal of which is achieved through iron chelation therapy. A commonly used iron chelator, deferiprone, has been found to be pharmacologically efficacious. However, important questions exist about the efficacy and safety of deferiprone compared to another iron chelator, desferrioxamine. OBJECTIVES: To summarise data from trials on the clinical efficacy and safety of deferiprone and to compare the clinical efficacy and safety of deferiprone for thalassaemia with desferrioxamine. SEARCH STRATEGY: We searched the Group's Haemoglobinopathies Trials Register, MEDLINE, EMBASE, Biological Abstracts, ZETOC, Current Controlled Trials and bibliographies of relevant publications. We contacted the manufacturers of deferiprone and desferrioxamine. Most recent searches: June 2006. SELECTION CRITERIA: Randomised controlled trials comparing deferiprone with another iron chelator; or comparing two schedules of deferiprone, in people with transfusion-dependent thalassaemia. DATA COLLECTION AND ANALYSIS: Two authors independently assessed trial quality and extracted data. Missing data were requested from the original investigators. MAIN RESULTS: Ten trials involving 398 people (range 10 to 144 people) were included. Nine trials compared deferiprone with desferrioxamine or a combination of deferiprone and desferrioxamine and one compared different schedules of deferiprone. There was little consistency between outcomes and little information to fully assess the methodological quality of most of the included trials. No trial reported long-term outcomes (mortality and end organ damage). There was no consistent effect on reduction of iron overload between all treatment comparisons, with the exception of urinary iron excretion in comparisons of deferiprone with desferrioxamine. An increase in iron excretion levels favoured deferiprone in one trial and desferrioxamine in three trials, even though measurement of urinary iron excretion underestimates total iron excretion by desferrioxamine.Adverse events were recorded in trials comparing deferiprone with desferrioxamine. There was evidence of adverse events in all treatment groups. Adverse events in one trial were significantly more likely with deferiprone than desferrioxamine, relative risk 2.24 (95% confidence interval 1.19 to 4.23). AUTHORS' CONCLUSIONS: We found no reason to change current treatment recommendations, namely deferiprone is indicated for treating iron overload in people with thalassaemia major when desferrioxamine is contraindicated or inadequate. However, there is an urgent need for adequately-powered, high quality trials comparing the overall clinical efficacy and long-term outcome of deferiprone with desferrioxamine.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.000

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 head0.093
GPT teacher head0.357
Teacher spread0.264 · 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 designSystematic review
Domainnot available
GenreReview

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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Citations41
Published2007
Admission routes1
Has abstractyes

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