The incidence of leukemia and mortality from sepsis in patients with severe congenital neutropenia receiving long-term G-CSF therapy
Bibliographic record
Abstract
In patients with severe congenital neutropenia (SCN), sepsis mortality is reduced by treatment with granulocyte colony-stimulating factor (G-CSF), but myelodsyplastic syndrome and acute myeloid leukemia (MDS/AML) have been reported. We studied 374 patients with SCN and 29 patients with Shwachman-Diamond syndrome (SDS) on long-term G-CSF enrolled in the Severe Chronic Neutropenia International Registry. In SCN, sepsis mortality was stable at 0.9% per year. The hazard of MDS/AML increased significantly over time, from 2.9% per year after 6 years to 8.0% per year after 12 years on G-CSF. After 10 years, the cumulative incidence was 8% for sepsis mortality and 21% for MDS/AML. A subgroup of SCN patients (29%) received more than the median dose of G-CSF (> or = 8 microg/kg/d), but achieved less than the median absolute neutrophil count (ANC) response (ANC < 2.188 x 10(9)/L [2188/microL] at 6-18 months). In these less-responsive patients, the cumulative incidence of adverse events was highest: after 10 years, 40% developed MDS/AML and 14% died of sepsis, compared with 11% and 4%, respectively, of more responsive patients whose ANC was above the median on doses of G-CSF below the median. Risk of MDS/AML may be similar in SDS and SCN. In less-responsive SCN patients, early hematopoietic stem cell transplantation may be a rational option.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".