Incidence and Burden of Lower-Risk Myelodysplastic Syndrome: A Nationwide Population Study
Bibliographic record
Abstract
BACKGROUND: Myelodysplastic syndromes (MDS) are hematological malignancies that primarily affect older individuals, often leading to anemia, which significantly impacts quality of life. Until now, the management of lower-risk MDS (LR-MDS) typically includes erythropoiesis-stimulating agents (ESAs) as first-line treatment, with transfusions becoming necessary in cases of ESA resistance. This study aimed to assess the incidence, prevalence, and clinical outcomes of LR-MDS patients in France, using the French National Health Data System (SNDS). MATERIALS AND METHODS: A retrospective cohort of 822 LR-MDS patients treated between 2018 and 2022 was analyzed, with patients classified based on transfusion dependency. RESULTS: Results showed a median LR-MDS extrapolated incidence of 5,850 patients per year in France (between 6.9 to 9.3 cases per 100,000 persons). Transfusion-dependent (TD) patients represented 32.5% of the cohort. TD patients had a significantly lower 2-year overall survival rate of 53% compared to 70% in nontransfusion-dependent (NTD) patients. Over the study period, 41.9% (N = 112) died out of 267 newly treated LR-MDS patients. Also, there were 88 events of death (with no progression), 26 estimated progression to HR-MDS and 11 progression to acute myeloid leukemia. CONCLUSION: These findings highlight the substantial burden of transfusion dependency in LR-MDS patients, emphasizing the need for improved therapeutic strategies.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".