Incidence rates of treatment-emergent adverse events and related hospitalization are reduced with azacitidine compared with conventional care regimens in older patients with acute myeloid leukemia
Bibliographic record
Abstract
Relative risks of treatment-emergent adverse events (TEAEs) and related hospitalization is most accurate when accounting for treatment exposure. AZA-AML-001 showed azacitidine (AZA) prolonged overall survival versus conventional care regimens (CCR) in older patients (≥65 years) with acute myeloid leukemia (AML) by 3.9 months. Preselection of CCR before study randomization allows evaluation of AZA safety in patient subgroups with similar clinical features. Within preselection groups, AZA exposure was greater than each CCR. Incidence rates (IRs; numbers of events normalized for drug exposure time) of hospitalizations and days in hospital for TEAEs per patient-year of exposure were to varying degrees lower with AZA versus each CCR. Overall survival was significantly prolonged with AZA versus best supportive care (BSC) in AZA-AML-001; this analysis showed 55% and 41% reductions in IRs of TEAE-related hospitalization and days in hospital, respectively, with AZA versus BSC. Older patients with AML unable to tolerate intensive therapy should be offered active low-intensity treatment.
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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 | 0.000 |
| 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".