Novel Tools for Diagnosis and Monitoring of AML
Bibliographic record
Abstract
In recent years, major advances in the understanding of acute myeloid leukemia (AML) pathogenesis, together with technological progress, have led us into a new era in the diagnosis and follow-up of patients with AML. A combination of immunophenotyping, cytogenetic and molecular studies are required for AML diagnosis, including the use of next-generation sequencing (NGS) gene panels to screen all genetic alterations with diagnostic, prognostic and/or therapeutic value. Regarding AML monitoring, multiparametric flow cytometry and quantitative PCR/RT-PCR are currently the most implemented methodologies for measurable residual disease (MRD) evaluation. Given the limitations of these techniques, there is an urgent need to incorporate new tools for MRD monitoring, such as NGS and digital PCR. This review aims to provide an overview of the different technologies used for AML diagnosis and MRD monitoring and to highlight the limitations and challenges of current versus emerging tools.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 teacher head, 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".