Does Time Matter From Diagnosis to Induction in Acute Myeloid Leukemia?
Bibliographic record
Abstract
Background: While molecular and cytogenetic testing may change prognosis and guide treatment intensity for patients with acute myeloid leukemia (AML), timing from diagnosis to treatment (TDT) on the other hand may impact treatment outcomes and survival. These considerations are sometimes at odds with each other given that molecular studies can take up to 2 weeks to result. Methods: A retrospective cohort analysis was conducted at SUNY Upstate University Hospital to examine the effect of TDT on complete remission (CR) and overall survival (OS). The subjects were at least 18 years old and diagnosed with AML and treated between January 2010 and June 2024. TDT was divided into three categories: chemotherapy induction within 1 - 5, 6 - 10, and 11+ days. Univariate Kaplan-Myer survival analysis and multivariate Cox regression model were performed. Results: AML. Chemotherapy induction began for 70% (n = 130) on days 1 - 5, 16% (n = 30) between days 6 - 10, and 14% (n = 27) on day 11 or after. The probability of achieving CR decreases for those who had induction 11+ days from diagnosis compared to those who had induction 1 to 5 days from diagnosis. This relationship was statistically significant (odds ratio = 0.32, 95% confidence interval (CI): 0.125 - 0.796; P = 0.003). However, no differences in OS and CR between TDT groups were seen when multivariate analysis was performed. Conclusion: Our retrospective study showed no difference in OS based on TDT groups, which supports clinicians' approach to await on comprehensive AML profiling for an optimal risk stratification at diagnosis and implementing best course of action.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".