Finding the right fit: assessment of fitness in AML
Bibliographic record
Abstract
In this issue of Blood Advances, Venditti et al 1 detail a set of recommendations from the European Leukemia Network (ELN) on fitness assessment in acute myeloid leukemia (AML).Based on the Grading of Recommendations Assessment, Development, and Evaluation methodology, statements 2 were assigned an evidence level and grade, followed by a 2-round Delphi consensus process on the level of agreement involving 31 hematologists with disease-specific expertise and patient representatives.In the last decade, both the complexity of AML treatment and potential considerations for fitness assessments have grown, with a lack of consensus on the required components.The development of both targeted and nonintensive therapies has shifted the thinking of treatment eligibility from a binary "fit" or "unfit" for intensive chemotherapy (IC) to a position where many patients may be eligible for at least lower-intensity treatment.These ELN guidelines therefore provide a critical tool to help define fitness/unfitness and support efforts to identify and categorize individual factors that contribute to these definitions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".