Monthly blood transfusions decrease after four months of azacitidine
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Azacitidine (AZA) improves overall survival and transfusion independence in patients with myelodysplastic syndrome (MDS). We aimed to quantify the reduction in red blood cell (RBC) transfusions and to determine when this reduction occurs, in MDS patients treated with AZA. MATERIALS AND METHODS: We performed a retrospective audit of changes in RBC transfusion burden in 51 patients with predominantly higher risk MDS (26.5% high risk, 51.0% intermediate-2) who received AZA. Transfusion requirements were audited 6 months prior to and up to 18 months after therapy initiation, and data were analysed using a generalized linear mixed model. RESULTS: At baseline, 30 patients (58.8%) were transfusion dependent (TD). Seventeen patients (56.7%) achieved transfusion independence (TI) by 18 months, and 8 of these patients (47.1%) achieved this response by 4 months on therapy. Achievement of TI was not consistently durable in these 17 patients, as 11 patients reverted to TD while on therapy. Meanwhile, 6 of 21 patients who were TI at baseline became TD on therapy. The monthly average of RBC units transfused decreased significantly beginning at 4 months, with a reduction from 2.50 units per month at baseline to 1.00 units per month at month 4. This 60% reduction was significant (P = 0.002) and sustained beyond 12 months. CONCLUSION: These results bolster the notion that AZA significantly reduces transfusion burden and resource utilization and illustrate the limitations of the current WHO erythroid response criteria which do not account for differing durability and fluctuations of response.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".