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Higher Erythroid Response Rates with Epoetin Alfa Versus Other Agents in Treatment-Naive Low/Int-1 Myelodysplastic Syndrome Patients: A Comparative Meta-Analysis

2008· article· en· W2994476126 on OpenAlexaff
Suneel Mundle, Patrick Lefèbvre, Francis Vekeman, Mei Sheng Duh, François Laliberté, Ruchi Rastogi, Victor Moyo

Bibliographic record

VenueBlood · 2008
Typearticle
Languageen
FieldMedicine
TopicErythropoietin and Anemia Treatment
Canadian institutionsGroup for Research in Decision Analysis
Fundersnot available
KeywordsMedicineInternal medicineAnemiaMyelodysplastic syndromesEpoetin alfaInternational Prognostic Scoring SystemMeta-analysisGastroenterologyOncology

Abstract

fetched live from OpenAlex

Abstract Background: The treatment of newly diagnosed low/intermediate-1 (low/int-1) myelodysplastic syndromes (MDS) varies significantly from observation only (until the patient develops transfusion dependence) to using specific agents like erythropoiesis-stimulating (ESA), demethylating, or immunomodulatory (IMiD) agents. To date no prospective comparative clinical trial has been conducted to evaluate relative efficacy of these agents. In the present literature meta-analysis, we compared the erythroid response (ER) rates achieved with epoetin alfa (EPO)-based therapy (monotherapy or in combination with other growth factors or non-ESAs) versus single agent/combination non-ESA therapies for the treatment of anemia in patients with MDS and specifically in low/int-1 risk category. Methods: Data extraction was performed on studies from PubMed and ASCO/ASH proceedings in MDS patients treated with EPO ± granulocyte-/granulocyte-macrophage colony stimulating factor (G/GM-CSF) or other non-ESAs (search cutoff date 9/30/07). To allow cross comparison, only studies using IWG or IWG-like ER criteria and treatment-naïve patients were selected. Patients were further stratified according to risk and non-ESA therapy. Pooled estimates of ER rates were calculated using random-effect (R-E) meta-analysis methods. Results: Of 790 studies identified, 37 were included in the present analysis. Among the 23 studies that included low/int-1 MDS patients, a majority of the patients in both the EPO-based (82.4%) and non-ESA (74.4%) groups had refractory anemia or refractory anemia with ringed sideroblasts, and baseline transfusion dependency rates were lower in the EPO-based (52.4%) than in the non-ESA (91.7%) groups. As shown in the Table, among all MDS patients regardless of risk category, EPO-based regimens had a higher ER rate when compared to IMiD-based therapies (p=0.0328), or demethylating agent-based therapies (p=0.1660). In the patients with low/int-1 risk disease, EPO-based therapy had a significantly better ER rate when compared to single-agent/combination non-ESA-based therapies (p=0.0015) and specifically, IMiD-based therapies (p=0.0231). The studies with demethylating agent-based therapies in the present analysis did not include low/int-1 MDS patients. Conclusions: These results suggest that, for appropriately selected treatment-naïve patients with low/int-1 MDS, among the currently available therapies, EPO-based regimens may yield a higher erythroid response than non-ESA therapies, with lenalidomide in del 5q patients being the only exception. Further prospective clinical trials are needed to determine relative clinical benefits with different agents in different MDS risk groups. Non-ESA based therapy MDS Study Group EPO-based therapy All mono + combination therapies IMiD-based therapy Demethylating agent-based therapy NA: not available No. of Studies All 19 18 9 2 Low/Int-1 Risk 14 9 4 NA Evaluable patients, n All 849 866 635 85 Low/Int-1 Risk 620 403 291 NA Overall ER, R-E% (95% CI) All 56.7 (49–65) 33.6 (23–44) 39.3 (26–53) 37.6 (12–63) Low/Int-1 Risk 53.8 (46–62) 35.1 (24–46) 42.4 (37–48) NA

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)0.014
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.985
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.022
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0150.048
Bibliometrics0.0030.004
Science and technology studies0.0000.001
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.108
GPT teacher head0.325
Teacher spread0.217 · 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.

Study designMeta-analysis
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

Citations0
Published2008
Admission routes1
Has abstractyes

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