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Record W1972544764 · doi:10.3747/co.22.2100

A Case of Transfusion Independence in a Patient with Myelodysplastic Syndrome Using Deferasirox, Sustained for Two Years after Stopping Therapy

2015· article· en· W1972544764 on OpenAlexaffvenue
David Sanford, Cyrus C. Hsia

Bibliographic record

VenueCurrent Oncology · 2015
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsLondon Health Sciences Centre
Fundersnot available
KeywordsDeferasiroxMedicineMyelodysplastic syndromesPediatricsIndependence (probability theory)Intensive care medicineFamily medicineInternal medicineThalassemia

Abstract

fetched live from OpenAlex

Patients with myelodysplastic syndrome (mds) experience clinical complications related to progressive marrow failure and have an increased risk of developing acute myeloid leukemia. Frequent red blood cell transfusion can lead to clinical iron overload and is associated with decreased survival in mds patients. Iron chelation therapy reduces markers of iron overload and prevents end-organ damage. Here, we present the case of a patient with low-risk mds with transfusional iron overload. He was treated for 2 years with an oral iron chelator, deferasirox, and after 12 months of treatment, he experienced a hemoglobin increase of more than 50 g/L, becoming transfusion-independent. He has remained transfusion-independent, with a normal hemoglobin level, for more than 2 years since stopping chelation therapy. Hematologic and erythroid responses have previously been reported in mds patients treated with iron chelation. The durability of our patient's response suggests that iron chelation might alter the natural history of mds in some 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 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)0.000
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: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0040.004
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 head0.115
GPT teacher head0.408
Teacher spread0.293 · 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".

Quick stats

Citations5
Published2015
Admission routes2
Has abstractyes

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